Initializes a new instance of the {@link com.bayesserver.inference.variableelimination.VariableEliminationInference} class, with the target Bayesian network.
The target {@link com.bayesserver.Network}.
Gets the evidence (case data, e.g. row in a database) used in a {@link com.bayesserver.inference.IInference#query query}.
The {@link com.bayesserver.inference.IInference#getQueryDistributions distributions} are only recalculated when {@link com.bayesserver.inference.IInference#query} is called, not each time evidence is changed.
Sets the evidence (case data, e.g. row in a database) used in a {@link com.bayesserver.inference.IInference#query query}.
The {@link com.bayesserver.inference.IInference#getQueryDistributions distributions} are only recalculated when {@link com.bayesserver.inference.IInference#query} is called, not each time evidence is changed.
The collection of distributions required from a {@link com.bayesserver.inference.IInference#query}. Only request those that you need.
Each distribution added, such as a {@link com.bayesserver.Table}, acts like a buffer. I.e. the values are populated by the {@link com.bayesserver.inference.IInference#query} method. This means that the same distributions can be resused across multiple calls to {@link com.bayesserver.inference.IInference#query}, without the need to recreate the distribution each time.
It is important not to request distributions you do not need, because the computations will take longer. For example, it is common to request all marginal probabilities, P(A), P(B), P(C), ... , P(Z) given the {@link com.bayesserver.inference.IEvidence evidence}, however if you only need to know P(A) and P(B) given the {@link com.bayesserver.inference.IEvidence evidence}, then limit the query to just these distributions.
Typically the distributions requested are a number of marginal propabilities, such as P(A), P(B), P(C) etc... given the {@link com.bayesserver.inference.IEvidence evidence}, however it is also possible to request distributions over more than one variable, so we might query P(A), P(B,C) given the {@link com.bayesserver.inference.IEvidence evidence}.
If you are performing a batch of queries, there is no need to remove those distributions that you require, however happen to have evidence set for a particular query.
The collection of distributions required from a {@link com.bayesserver.inference.IInference#query}. Only request those that you need.
Each distribution added, such as a {@link com.bayesserver.Table}, acts like a buffer. I.e. the values are populated by the {@link com.bayesserver.inference.IInference#query} method. This means that the same distributions can be resused across multiple calls to {@link com.bayesserver.inference.IInference#query}, without the need to recreate the distribution each time.
It is important not to request distributions you do not need, because the computations will take longer. For example, it is common to request all marginal probabilities, P(A), P(B), P(C), ... , P(Z) given the {@link com.bayesserver.inference.IEvidence evidence}, however if you only need to know P(A) and P(B) given the {@link com.bayesserver.inference.IEvidence evidence}, then limit the query to just these distributions.
Typically the distributions requested are a number of marginal propabilities, such as P(A), P(B), P(C) etc... given the {@link com.bayesserver.inference.IEvidence evidence}, however it is also possible to request distributions over more than one variable, so we might query P(A), P(B,C) given the {@link com.bayesserver.inference.IEvidence evidence}.
If you are performing a batch of queries, there is no need to remove those distributions that you require, however happen to have evidence set for a particular query.
An exact inference algorithm for Bayesian networks and Dynamic Bayesian networks, loosely based on the Variable Elimination algorithm.
com.bayesserver.inference.relevancetree.RelevanceTreeInference
com.bayesserver.inference.IInference