NetworkSamples samples = Keanu.Sampling.NUTS.withDefaultConfig().getPosteriorSamples( model, aVertex,
public static io.improbable.keanu.algorithms.mcmc.nuts.NUTS withDefaultConfig() { return withDefaultConfig(KeanuRandom.getDefaultRandom()); }
@Test public void youCanCreateADefaultNUTSSampler() { NUTS nuts = Keanu.Sampling.NUTS.withDefaultConfig(); }
/** * @param model network for which to choose sampling algorithm. * @param random the random number generator. * @return recommended sampling algorithm for this network. */ public PosteriorSamplingAlgorithm withDefaultConfigFor(KeanuProbabilisticModel model, KeanuRandom random) { if (DifferentiableChecker.isDifferentiableWrtLatents(model.getLatentOrObservedVertices())) { return Keanu.Sampling.NUTS.withDefaultConfig(random); } else { return Keanu.Sampling.MetropolisHastings.withDefaultConfigFor(model, random); } } }
private static void nutsExample() { BayesianNetwork bayesNet = null; ProbabilisticModel model = null; KeanuRandom random = null; //%%SNIPPET_START%% InfNuts NetworkSamples posteriorSamples = Keanu.Sampling.NUTS.withDefaultConfig().getPosteriorSamples( model, model.getLatentVariables(), 2000 ); //%%SNIPPET_END%% InfNuts } }