io.improbable.keanu.vertices

Interface Probabilistic<T>

All Superinterfaces:

Observable<T>, Samplable<T>

All Known Subinterfaces:

ProbabilisticBoolean, ProbabilisticDouble, ProbabilisticInteger

All Known Implementing Classes:

BernoulliVertex, BetaVertex, BinomialVertex, CategoricalVertex, CauchyVertex, ChiSquaredVertex, DirichletVertex, ExponentialVertex, GammaVertex, GaussianVertex, GeometricVertex, HalfCauchyVertex, HalfGaussianVertex, InverseGammaVertex, KDEVertex, LaplaceVertex, LogisticVertex, LogNormalVertex, MultinomialVertex, MultivariateGaussianVertex, ParetoVertex, PoissonVertex, SmoothUniformVertex, StudentTVertex, TriangularVertex, UniformIntVertex, UniformVertex

public interface Probabilistic<T>
extends Observable<T>, Samplable<T>

Method Summary

Modifier and Type	Method and Description
java.util.Map<Vertex,DoubleTensor>	dLogProb(T atValue, java.util.Set<? extends Vertex> withRespectTo) The partial derivatives of the natural log prob.
default java.util.Map<Vertex,DoubleTensor>	dLogProb(T atValue, Vertex... withRespectTo)
default java.util.Map<Vertex,DoubleTensor>	dLogProbAtValue(java.util.Set<? extends Vertex> withRespectTo)
default java.util.Map<Vertex,DoubleTensor>	dLogProbAtValue(Vertex... withRespectTo)
T	getValue()
static <V extends Vertex & Probabilistic> java.util.List<V>	keepOnlyProbabilisticVertices(java.lang.Iterable<? extends Vertex> vertices)
double	logProb(T value) This is the natural log of the probability at the supplied value.
default double	logProbAtValue()

Methods inherited from interface io.improbable.keanu.vertices.Observable

getObservedValue, isObserved, observe, unobserve

Methods inherited from interface io.improbable.keanu.vertices.Samplable

sample, sample

Method Detail

logProb

double logProb(T value)

This is the natural log of the probability at the supplied value. In the case of continuous vertices, this is actually the log of the density, which will differ from the probability by a constant.

Parameters:

value - The supplied value.

Returns:

The natural log of the probability function at the supplied value. For continuous variables this is called the PDF (probability density function). For discrete variables this is called the PMF (probability mass function).

dLogProb

java.util.Map<Vertex,DoubleTensor> dLogProb(T atValue,
                                            java.util.Set<? extends Vertex> withRespectTo)

The partial derivatives of the natural log prob.

Parameters:

atValue - at a given value

withRespectTo - list of parents to differentiate with respect to

Returns:

the partial derivatives of the log of the probability function at the supplied value. For continuous variables this is called the PDF (probability density function). For discrete variables this is called the PMF (probability mass function).

dLogProb

default java.util.Map<Vertex,DoubleTensor> dLogProb(T atValue,
                                                    Vertex... withRespectTo)

getValue

T getValue()

logProbAtValue

default double logProbAtValue()

dLogProbAtValue

default java.util.Map<Vertex,DoubleTensor> dLogProbAtValue(java.util.Set<? extends Vertex> withRespectTo)

dLogProbAtValue

default java.util.Map<Vertex,DoubleTensor> dLogProbAtValue(Vertex... withRespectTo)

keepOnlyProbabilisticVertices

static <V extends Vertex & Probabilistic> java.util.List<V> keepOnlyProbabilisticVertices(java.lang.Iterable<? extends Vertex> vertices)