Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with [count] variables specified in [variableContexts]. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with [count] variables specified in [variableContexts]. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the variables of a single node at the specified time. Variables are assumed to be head variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables at a particular time. Variables are assumed to be head variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables. Variables are assumed to be head variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables. Variables are assumed to be head variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the variables of a single node. Variables are assumed to be head variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with a single variable. The variable is assumed to be a head variable. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class from a single {@link com.bayesserver.VariableContext}. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with a single variable at the specified time. The variable is assumed to be a head variable. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class, copying the source distribution. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class, copying the source distribution but shifting any times by the specified number of units.
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
The variable contexts containing the distribution variables.
Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with [count] variables specified in [variableContexts]. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with [count] variables specified in [variableContexts]. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the variables of a single node at the specified time. Variables are assumed to be head variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables at a particular time. Variables are assumed to be head variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables. Variables are assumed to be head variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables. Variables are assumed to be head variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the variables of a single node. Variables are assumed to be head variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with a single variable. The variable is assumed to be a head variable. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class from a single {@link com.bayesserver.VariableContext}. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with a single variable at the specified time. The variable is assumed to be a head variable. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class, copying the source distribution. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class, copying the source distribution but shifting any times by the specified number of units.
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
The variable contexts containing the distribution variables.
Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with [count] variables specified in [variableContexts]. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with [count] variables specified in [variableContexts]. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the variables of a single node at the specified time. Variables are assumed to be head variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables at a particular time. Variables are assumed to be head variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables. Variables are assumed to be head variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables. Variables are assumed to be head variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the variables of a single node. Variables are assumed to be head variables. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with a single variable. The variable is assumed to be a head variable. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class from a single {@link com.bayesserver.VariableContext}. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with a single variable at the specified time. The variable is assumed to be a head variable. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class, copying the source distribution. Initializes a new instance of the {@link com.bayesserver.CLGaussian} class, copying the source distribution but shifting any times by the specified number of units.
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
The variable contexts containing the distribution variables.
Overrides the Head or Tail value found in each {@link com.bayesserver.VariableContext}.
Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with [count] variables specified in [variableContexts].
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
The variable contexts containing the distribution variables.
The number of items to include from [variableContexts].
Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with [count] variables specified in [variableContexts].
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
The variable contexts containing the distribution variables.
The number of items to include from [variableContexts].
Overrides the Head or Tail value found in each {@link com.bayesserver.VariableContext}.
Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the variables of a single node at the specified time. Variables are assumed to be head variables.
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
The node whose variables will belong to the new distribution.
The time for any temporal nodes/variables.
Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables at a particular time. Variables are assumed to be head variables.
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
The variables for the new distribution.
The time for any temporal nodes/variables.
Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables.
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
The variables for the new distribution.
Overrides the Head or Tail value found in each {@link com.bayesserver.VariableContext}.
Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables.
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
The variables for the new distribution.
The time for any temporal nodes/variables.
Overrides the Head or Tail value found in each {@link com.bayesserver.VariableContext}.
Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables. Variables are assumed to be head variables.
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the specified variables. Variables are assumed to be head variables.
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
The variables for the new distribution.
Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with the variables of a single node. Variables are assumed to be head variables.
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
The node whose variables will belong to the new distribution.
Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with a single variable. The variable is assumed to be a head variable.
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
The variable that will belong to the new distribution.
Initializes a new instance of the {@link com.bayesserver.CLGaussian} class from a single {@link com.bayesserver.VariableContext}.
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
The variable context.
Initializes a new instance of the {@link com.bayesserver.CLGaussian} class with a single variable at the specified time. The variable is assumed to be a head variable.
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
The variable that will belong to the new distribution.
The time for any temporal nodes/variables.
Initializes a new instance of the {@link com.bayesserver.CLGaussian} class, copying the source distribution.
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
The distribution to copy.
Initializes a new instance of the {@link com.bayesserver.CLGaussian} class, copying the source distribution but shifting any times by the specified number of units.
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
Each variable, if it belongs to a temporal node can have an associated time. A variable is also marked as either head or tail. Head variables are those on the left, and tail variables are those on the right in the expression P(A|B).
The distribution to copy.
The number of units to adjust any times associated with variables.
Gets the current owner, if assigned to a node. A distribution cannot be modified when it is assigned to a node.
The owner, or null if not assigned to a node.
Gets the collection of continuous head variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
Note that head variables are those that appear to the left of the bar in the expression P(A|B) and tail variables are those to the right.
Continuous head variables sorted by time, and the order in which variables were created.
Gets the collection of continuous tail variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
Note that head variables are those that appear to the left of the bar in the expression P(A|B) and tail variables are those to the right.
Continuous tail variables sorted by time and the order in which variables were created.
Gets the collection of variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
Variables sorted by time and the order in which variables were created.
Gets the {@link com.bayesserver.Table} which specifies the distribution over any discrete variables.
The table.
Creates a copy of the distribution. The new distribution will not have an owner.
A copy of this instance.
Creates a copy of the distribution, and shifts any times associated with variables by the specified amount. The new distribution will not have an owner.
The amount to shift any times present in the distribution. Can be negative.
A copy of this instance, with shifted times.
Copies the values from the [source] distribution to this instance. The variable counts between distributions must match but the variable contexts need not be equal.
The source distribution from which values are copied.
Creates a new distribution by dividing this instance by the [subset]. Also known as the complement.
If the resulting distribution were subsequently multiplied by [subset], the result would be equivalent to this instance.
The subset to divide by.
The new distribution (complement).
Creates a new distribution by dividing this instance by the [subset]. Also known as the complement.
If the resulting distribution were subsequently multiplied by [subset], the result would be equivalent to this instance.
The subset to divide by.
The new distribution (complement).
Gets the covariance of the Gaussian distribution at the specified [index] in the {@link com.bayesserver.Table} of discrete combinations.
The index into the discrete table of combinations. If no discrete variables are present in the distribution, index will always be 0.
The position of the first continuous head variable.
The position of the second continuous head variable.
The covariance entry.
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
A second continuous head variable from H in the expression P(H) or P(H|T).
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The covariance value.
Gets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
A continuous head variable from H in the expression P(H) or P(H|T).
A second continuous head variable from H in the expression P(H) or P(H|T).
The covariance value.
Gets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the first continuous head variable, or null if not a temporal variable.
A second continuous head variable from H in the expression P(H) or P(H|T).
The time of the second continuous head variable, or null if not a temporal variable.
The covariance value.
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
A second continuous head variable from H in the expression P(H) or P(H|T).
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The covariance value.
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the first continuous head variable, or null if not a temporal variable.
A second continuous head variable from H in the expression P(H) or P(H|T).
The time of the second continuous head variable, or null if not a temporal variable.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The covariance value.
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the first continuous head variable, or null if not a temporal variable.
A second continuous head variable from H in the expression P(H) or P(H|T).
The time of the second continuous head variable, or null if not a temporal variable.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The covariance value.
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
A second continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The covariance value.
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
A second continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The covariance value.
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
A second continuous head variable from H in the expression P(H) or P(H|T).
The discrete combination (mixture) identified by the position of the iterator.
The covariance value.
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the first continuous head variable, or null if not a temporal variable.
A second continuous head variable from H in the expression P(H) or P(H|T).
The time of the second continuous head variable, or null if not a temporal variable.
The discrete combination (mixture) identified by the position of the iterator.
The covariance value.
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
A second continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
The discrete combination (mixture) identified by the position of the iterator.
The covariance value.
Gets the mean of the Gaussian distribution at the specified [index] in the {@link com.bayesserver.Table} of discrete combinations.
The index into the discrete table of combinations. If no discrete variables are present in the distribution, index will always be 0.
The position of the required continuous head variable.
The mean value.
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
A continuous head variable from H in the expression P(H) or P(H|T).
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The mean value.
Gets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
A continuous head variable from H in the expression P(H) or P(H|T).
The mean value.
Gets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable and time.
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
The mean value.
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The mean value.
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The mean value.
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
A continuous head variable from H in the expression P(H) or P(H|T).
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The mean value.
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The mean value.
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The mean value.
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
A continuous head variable from H in the expression P(H) or P(H|T).
The discrete combination (mixture) identified by the position of the iterator.
The mean value.
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
The discrete combination (mixture) identified by the position of the iterator.
The mean value.
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
The discrete combination (mixture) identified by the position of the iterator.
The mean value.
Gets the variance of the Gaussian distribution at the specified [index] in the {@link com.bayesserver.Table} of discrete combinations.
The index into the discrete table of combinations. If no discrete variables are present in the distribution, index will always be 0.
The position of the required continuous head variable.
The variance.
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The variance value.
Gets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
A continuous head variable from H in the expression P(H) or P(H|T).
The variance value.
Gets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
The variance value.
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The variance value.
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The variance value.
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The variance value.
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The variance value.
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The variance value.
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
The discrete combination (mixture) identified by the position of the iterator.
The variance value.
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
The discrete combination (mixture) identified by the position of the iterator.
The variance value.
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
The discrete combination (mixture) identified by the position of the iterator.
The variance value.
Gets the weight (regression coefficient) of the Gaussian distribution at the specified [index] in the {@link com.bayesserver.Table} of discrete combinations.
The index into the discrete table of combinations. If no discrete variables are present in the distribution, index will always be 0.
The position of the required continuous head variable.
The position of the required continuous tail variable.
The weight / regression coefficient.
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H|T).
A continuous tail variable from T in the expression P(H|T).
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The weight/regression coefficient.
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
A continuous tail variable from T in the expression P(H|T).
The time of the continuous tail variable, or null if not a temporal variable.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The weight/regression coefficient.
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
A continuous head variable and time (if any) from H in the expression P(H|T).
A continuous tail variable and time (if any) from T in the expression P(H|T).
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The weight/regression coefficient.
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H|T).
A continuous tail variable from T in the expression P(H|T).
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The weight/regression coefficient.
Gets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
A continuous head variable from H in the expression P(H|T).
A continuous tail variable from T in the expression P(H|T).
The weight/regression coefficient.
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
A continuous tail variable from T in the expression P(H|T).
The time of the continuous tail variable, or null if not a temporal variable.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The weight/regression coefficient.
Gets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
A continuous head variable from H in the expression P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
A continuous tail variable from T in the expression P(H|T).
The time of the continuous tail variable, or null if not a temporal variable.
The weight/regression coefficient.
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
A continuous head variable and time (if any) from H in the expression P(H|T).
A continuous tail variable and time (if any) from T in the expression P(H|T).
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
The weight/regression coefficient.
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H|T).
A continuous tail variable from T in the expression P(H|T).
The discrete combination (mixture) identified by the position of the iterator.
The weight/regression coefficient.
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
A continuous tail variable from T in the expression P(H|T).
The time of the continuous tail variable, or null if not a temporal variable.
The discrete combination (mixture) identified by the position of the iterator.
The weight/regression coefficient.
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
A continuous head variable and time (if any) from H in the expression P(H|T).
A continuous tail variable from T in the expression P(H|T).
The discrete combination (mixture) identified by the position of the iterator.
The weight/regression coefficient.
Calculates the distribution which results from instantiating a number of variables.
[values] should contain one entry for each
The instantiated values. Entries can be null.
The instantiated distribution.
Calculates the distribution which results from instantiating a particular variable.
The variable to instantiate.
The instantiated value.
The instantiated distribution.
Calculates the distribution which results from instantiating a particular variable at a specified time.
The variable to instantiate.
The instantiated value.
The time associated with the variable. Can be null.
The instantiated distribution.
Instantiates discrete variables.
A discrete value (or null) for each discrete variable in the Gaussian table.
The instantiated distribution.
Calculates the distribution which results from instantiating a particular continuous head variable at a specified time.
The variable to instantiate.
The instantiated value.
The time associated with the variable. Can be null.
The instantiated distribution.
Calculates the distribution which results from instantiating a particular continuous head variable at a specified time.
The variable to instantiate.
The instantiated value.
The time associated with the variable. Can be null.
A buffer of length {@link com.bayesserver.Table#size}that is filled with the log of the pdf values calculated during instantiation. Can be null.
The instantiated distribution.
Instantiates all continuous head variable contexts.
The values for the continuous head variable contexts.
Optional array of length {@link com.bayesserver.Table#size} that is filled with the logged pdf values, useful when pdf values are zero.
The instantiated distribution.
Instantiates continuous head variable contexts.
The value (or null) for each continuous head variable context.
Optional array of length {@link com.bayesserver.Table#size} that is filled with the logged pdf values, useful when pdf values are zero.
The instantiated distribution.
Calculates the distribution which results from instantiating continuous tail variables.
The value (or null) for each continuous tail.
The instantiated distribution.
Marginalizes (integrates) the [superset] into this instance.
A distribution whose variables form a superset of the variables in this instance.
Marginalizes (integrates) the [superset] into this instance.
A distribution whose variables form a superset of the variables in this instance.
The propagation method to use during marginalization.
Marginalizes (sums/integrates) the [superset] into this instance.
A {@link com.bayesserver.CLGaussian} whose variables form a superset of the variables in this instance.
Marginalizes (sums/integrates) out all continuous variables from this instance into the specified table.
A {@link com.bayesserver.Table} whose variables form a subset of discrete variables in this instance.
Marginalizes (sums/integrates) out all continuous variables from this instance into the specified table.
A {@link com.bayesserver.Table} whose variables form a subset of discrete variables in this instance.
The propagation method to use during marginalization.
Multiplies this instance by another {@link com.bayesserver.CLGaussian} distribution.
The distribution to combine.
The combined distribution.
Multiplies this instance by another distribution.
The distribution to combine.
The combined distribution.
Resets all mean, covariance and weight entries to zero.
Sets the covariance value of the Gaussian distribution at the specified [index] in the {@link com.bayesserver.Table} of discrete combinations.
The index into the discrete table of combinations. If no discrete variables are present in the distribution, index will always be 0.
The position of the first continuous head variable.
The position of the second continuous head variable.
The covariance value to copy.
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
A second continuous head variable from H in the expression P(H) or P(H|T).
The covariance value.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the first continuous head variable, or null if not a temporal variable.
A second continuous head variable from H in the expression P(H) or P(H|T).
The time of the second continuous head variable, or null if not a temporal variable.
The covariance value.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
A second continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
The covariance value.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
A second continuous head variable from H in the expression P(H) or P(H|T).
The covariance value.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the first continuous head variable, or null if not a temporal variable.
A second continuous head variable from H in the expression P(H) or P(H|T).
The time of the second continuous head variable, or null if not a temporal variable.
The covariance value.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB]
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the first continuous head variable, or null if not a temporal variable.
A second continuous head variable from H in the expression P(H) or P(H|T).
The time of the second continuous head variable, or null if not a temporal variable.
The covariance value.
Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB]
A continuous head variable from H in the expression P(H) or P(H|T).
A second continuous head variable from H in the expression P(H) or P(H|T).
The covariance value.
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
A second continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
The covariance value.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
A second continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
The covariance value.
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
A second continuous head variable from H in the expression P(H) or P(H|T).
The covariance value.
The discrete combination (mixture) identified by the position of the iterator.
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the first continuous head variable, or null if not a temporal variable.
A second continuous head variable from H in the expression P(H) or P(H|T).
The time of the second continuous head variable, or null if not a temporal variable.
The covariance value.
The discrete combination (mixture) identified by the position of the iterator.
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
A second continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
The covariance value.
The discrete combination (mixture) identified by the position of the iterator.
Sets the mean value of the Gaussian distribution at the specified [index] in the {@link com.bayesserver.Table} of discrete combinations.
The index into the discrete table of combinations. If no discrete variables are present in the distribution, index will always be 0.
The position of the required continuous head variable.
The mean value.
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
A continuous head variable from H in the expression P(H) or P(H|T).
The mean value.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
The mean value.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
The mean value.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
A continuous head variable from H in the expression P(H) or P(H|T).
The mean value.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
A continuous head variable from H in the expression P(H) or P(H|T).
The mean value.
Sets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
The mean value.
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
The mean value.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
The mean value.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
A continuous head variable from H in the expression P(H) or P(H|T).
The mean value.
The discrete combination (mixture) identified by the position of the iterator.
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
The mean value.
The discrete combination (mixture) identified by the position of the iterator.
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
The mean value.
The discrete combination (mixture) identified by the position of the iterator.
Sets the variance value of the Gaussian distribution at the specified [index] in the {@link com.bayesserver.Table} of discrete combinations.
The index into the discrete table of combinations. If no discrete variables are present in the distribution, index will always be 0.
The position of the required continuous head variable.
The variance value to set.
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
The variance value.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
The variance value.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
The variance value.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
The variance value.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
A continuous head variable from H in the expression P(H) or P(H|T).
The variance value.
Sets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
The variance value.
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
The variance value.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
The variance value.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
The variance value.
The discrete combination (mixture) identified by the position of the iterator.
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H) or P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
The variance value.
The discrete combination (mixture) identified by the position of the iterator.
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
A continuous head variable and time (if any) from H in the expression P(H) or P(H|T).
The variance value.
The discrete combination (mixture) identified by the position of the iterator.
Sets the weight/regression coefficient of the Gaussian distribution at the specified [index] in the {@link com.bayesserver.Table} of discrete combinations.
The index into the discrete table of combinations. If no discrete variables are present in the distribution, index will always be 0.
The position of the required continuous head variable.
The position of the required continuous tail variable.
The weight to copy.
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H|T).
A continuous tail variable from T in the expression P(H|T).
The weight/regression coefficient.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
A continuous tail variable from T in the expression P(H|T).
The time of the continuous tail variable, or null if not a temporal variable.
The weight/regression coefficient.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
A continuous head variable and associated time (if any) from H in the expression P(H|T).
A continuous tail variable and associated time (if any) from T in the expression P(H|T).
The weight/regression coefficient.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H|T).
A continuous tail variable from T in the expression P(H|T).
The weight/regression coefficient.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
A continuous head variable from H in the expression P(H|T).
A continuous tail variable from T in the expression P(H|T).
The weight/regression coefficient.
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
A continuous tail variable from T in the expression P(H|T).
The time of the continuous tail variable, or null if not a temporal variable.
The weight/regression coefficient.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
A continuous head variable from H in the expression P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
A continuous tail variable from T in the expression P(H|T).
The time of the continuous tail variable, or null if not a temporal variable.
The weight/regression coefficient.
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
A continuous head variable and associated time (if any) from H in the expression P(H|T).
A continuous tail variable and associated time (if any) from T in the expression P(H|T).
The weight/regression coefficient.
The discrete combination (mixture). Can be empty if this distribution has no discrete variables (i.e. the Gaussian is not a mixture of Gaussians).
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H|T).
A continuous tail variable from T in the expression P(H|T).
The weight/regression coefficient.
The discrete combination (mixture) identified by the position of the iterator.
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
A continuous head variable from H in the expression P(H|T).
The time of the continuous head variable, or null if not a temporal variable.
A continuous tail variable from T in the expression P(H|T).
The time of the continuous tail variable, or null if not a temporal variable.
The weight/regression coefficient.
The discrete combination (mixture) identified by the position of the iterator.
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
A continuous head variable and associated time (if any) from H in the expression P(H|T).
A continuous tail variable and associated time (if any) from T in the expression P(H|T).
The weight/regression coefficient.
The discrete combination (mixture) identified by the position of the iterator.
Shifts any times associated with the distribution variables by the specified number of time units.
The number of time units to shift. Can be negative if required.
Represents a Conditional Linear Gaussian probability distribution.
The distribution contains a {@link com.bayesserver.Table} distribution which represents any discrete combinations, and for each combination there exists a multivariate Gaussian distribution and weight/regression coefficients. Note that head variables are those that appear to the left of the bar in the expression P(A|B) and tail variables are those to the right.