com.cra.figaro.experimental.particlebp
ProbQueryParticleBeliefPropagation
Instantiates the appropriate BP algorithm for the current time step.
Instantiates the appropriate BP algorithm for the current time step.
Runs the BP algorithm at the current time step.
Runs the BP algorithm at the current time step.
BP algorithm associated with this time step.
BP algorithm associated with this time step.
Called when the algorithm is killed.
Called when the algorithm is killed. By default, does nothing. Can be overridden.
Return an estimate of the marginal probability distribution over the target that lists each element with its probability.
Return an estimate of the marginal probability distribution over the target that lists each element with its probability. The result is a lazy stream. It is up to the algorithm how the stream is ordered.
Return an estimate of the expectation of the function under the marginal probability distribution of the target.
Return an estimate of the expectation of the function under the marginal probability distribution of the target.
Return an estimate of the probability of the predicate under the marginal probability distribution of the target.
Return an estimate of the probability of the predicate under the marginal probability distribution of the target.
Universe associated with this algorithm.
Universe associated with this algorithm.
By default, implementations that inherit this trait have no debug information.
By default, implementations that inherit this trait have no debug information. Override this if you want a debugging option.
The density estimator that will estimate the density of a particle.
The density estimator that will estimate the density of a particle. used for resampling.
The algorithm to compute probability of specified evidence in a dependent universe.
The algorithm to compute probability of specified evidence in a dependent universe. We use () => Double to represent this algorithm instead of an instance of ProbEvidenceAlgorithm. Typical usage is to return the result of ProbEvidenceAlgorithm.computeProbEvidence when invoked.
A list of universes that depend on this universe such that evidence on those universes should be taken into account in this universe.
A list of universes that depend on this universe such that evidence on those universes should be taken into account in this universe.
Return an estimate of the marginal probability distribution over the target that lists each element with its probability.
Return an estimate of the marginal probability distribution over the target that lists each element with its probability. The result is a lazy stream. It is up to the algorithm how the stream is ordered. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.
Return an estimate of the expectation of the function under the marginal probability distribution of the target.
Return an estimate of the expectation of the function under the marginal probability distribution of the target. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.
Getting factors for PBP returns an empty list, since all of the factor creation is handled inside of the BP instances
Getting factors for PBP returns an empty list, since all of the factor creation is handled inside of the BP instances
Get the elements that are needed by the query target variables and the evidence variables.
Get the elements that are needed by the query target variables and the evidence variables. Also compute the values of those variables to the given depth. Only get factors for elements that are actually used by the target variables. This is more efficient. Also, it avoids problems when values of unused elements have not been computed.
In addition to getting all the needed elements, it determines if any of the conditioned, constrained, or dependent universe parent elements has * in its range. If any of these elements has * in its range, the lower and upper bounds of factors will be different, so we need to compute both. If they don't, we don't need to compute bounds.
Called when the algorithm is started before running any steps.
Called when the algorithm is started before running any steps. By default, does nothing. Can be overridden.
Kill the algorithm so that it is inactive.
Kill the algorithm so that it is inactive. It will no longer be able to provide answers.Throws AlgorithmInactiveException if the algorithm is not active.
Return the mean of the probability density function for the given continuous element.
Return the mean of the probability density function for the given continuous element.
A particle generator to generate particles and do resampling.
A particle generator to generate particles and do resampling.
Return an element representing the posterior probability distribution of the given element.
Return an element representing the posterior probability distribution of the given element.
Variable that if set to true, will preserve parts of the factor graph that cannot change during resampling.
Variable that if set to true, will preserve parts of the factor graph that cannot change during resampling. This will preserve the messages in those parts of the factor graph. This feature is experimental and not guaranteed to work currently. Default is false.
Return an estimate of the probability that the target produces the value.
Return an estimate of the probability that the target produces the value. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.
Return an estimate of the probability of the predicate under the marginal probability distribution of the target.
Return an estimate of the probability of the predicate under the marginal probability distribution of the target. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.
Resume the computation of the algorithm, if it has been stopped.
Resume the computation of the algorithm, if it has been stopped. Throws AlgorithmInactiveException if the algorithm is not active.
Runs this particle belief propagation algorithm for one iteration.
Runs this particle belief propagation algorithm for one iteration. An iteration here is one iteration of the outer loop. This means that the inner BP loop may run several iterations.
Since BP uses division to compute messages, the semiring has to have a division function defined
Since BP uses division to compute messages, the semiring has to have a division function defined
Start the algorithm and make it active.
Start the algorithm and make it active. After it returns, the algorithm must be ready to provide answers. Throws AlgorithmActiveException if the algorithm is already active.
Elements towards which queries are directed.
Elements towards which queries are directed. By default, these are the target elements.
Stop the algorithm from computing.
Stop the algorithm from computing. The algorithm is still ready to provide answers after it returns. Throws AlgorithmInactiveException if the algorithm is not active.
Target elements that should not be eliminated but should be available for querying.
Target elements that should not be eliminated but should be available for querying.
The universe on which this belief propagation algorithm should be applied.
The universe on which this belief propagation algorithm should be applied.
Return the variance of the probability density function for the given continuous element.
Return the variance of the probability density function for the given continuous element.
Class to implement a probability query BP algorithm