The number of iterations to run before samples are collected
The number of iterations to perform between collecting samples
A sample is a map from elements to their values.
A sample is a map from elements to their values.
Get the acceptance ratio for the sampler
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.
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.
Set this flag to true to obtain debugging information
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.
Number of samples taken.
Number of samples taken.
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
Return an element representing the posterior probability distribution of the given element
Return an element representing the posterior probability distribution of the given element
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.
Produce a single sample.
Produce a single sample.
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.
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.
Test Metropolis-Hastings by repeatedly running a single step from the same initial state.
Test Metropolis-Hastings by repeatedly running a single step from the same initial state. For each of a set of predicates, the fraction of times the predicate is satisfied by the resulting state is returned. By the resulting state, we mean the new state if it is accepted and the original state if not.
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
Metropolis-Hastings samplers.