io.improbable.keanu.algorithms.mcmc

Class NUTS

java.lang.Object

io.improbable.keanu.algorithms.mcmc.NUTS

All Implemented Interfaces:

PosteriorSamplingAlgorithm

public class NUTS
extends java.lang.Object
implements PosteriorSamplingAlgorithm

Algorithm 6: "No-U-Turn Sampler with Dual Averaging". The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo https://arxiv.org/pdf/1111.4246.pdf

Constructor Summary

Constructors

Constructor and Description
NUTS()

Method Summary

Modifier and Type	Method and Description
NetworkSamples	getPosteriorSamples(BayesianNetwork bayesNet, java.util.List<? extends Vertex> sampleFromVertices, int sampleCount) Sample from the posterior of a Bayesian Network using the No-U-Turn-Sampling algorithm
static NUTS	withDefaultConfig()
static NUTS	withDefaultConfig(KeanuRandom random)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface io.improbable.keanu.algorithms.PosteriorSamplingAlgorithm

getPosteriorSamples

Constructor Detail

NUTS

public NUTS()

Method Detail

withDefaultConfig

public static NUTS withDefaultConfig()

withDefaultConfig

public static NUTS withDefaultConfig(KeanuRandom random)

getPosteriorSamples

public NetworkSamples getPosteriorSamples(BayesianNetwork bayesNet,
                                          java.util.List<? extends Vertex> sampleFromVertices,
                                          int sampleCount)

Sample from the posterior of a Bayesian Network using the No-U-Turn-Sampling algorithm

Specified by:

getPosteriorSamples in interface PosteriorSamplingAlgorithm

Parameters:

bayesNet - the bayesian network to sample from

sampleFromVertices - the vertices inside the bayesNet to sample from

sampleCount - the number of samples to take

Returns:

Samples taken with NUTS