io.improbable.keanu.vertices.dbl.probabilistic

Class ChiSquaredVertex

java.lang.Object

io.improbable.keanu.vertices.Vertex<DoubleTensor>

io.improbable.keanu.vertices.dbl.DoubleVertex

io.improbable.keanu.vertices.dbl.probabilistic.ChiSquaredVertex

All Implemented Interfaces:

io.improbable.keanu.kotlin.DoubleOperators<DoubleVertex>, io.improbable.keanu.kotlin.Operators<DoubleVertex>, Differentiable, ProbabilisticDouble, Observable<DoubleTensor>, Probabilistic<DoubleTensor>

public class ChiSquaredVertex
extends DoubleVertex
implements ProbabilisticDouble

Constructor Summary

Constructors

Constructor and Description
ChiSquaredVertex(int k)
ChiSquaredVertex(int[] tensorShape, int k)
ChiSquaredVertex(int[] tensorShape, IntegerVertex k) One k that must match a proposed tensor shape of ChiSquared
ChiSquaredVertex(IntegerTensor k) One to one constructor for mapping some shape of k to a matching shaped ChiSquared.

Method Summary

Modifier and Type	Method and Description
java.util.Map<Vertex,DoubleTensor>	dLogProb(DoubleTensor value, java.util.Set<? extends Vertex> withRespectTo) The partial derivatives of the natural log prob.
double	logProb(DoubleTensor value) This is the natural log of the probability at the supplied value.
DoubleTensor	sample(KeanuRandom random)

Methods inherited from class io.improbable.keanu.vertices.dbl.DoubleVertex

abs, acos, asin, atan, atan2, calculateDualNumber, ceil, concat, cos, div, div, divideBy, divideBy, equalTo, exp, floor, getValue, greaterThan, greaterThanOrEqualTo, lambda, lambda, lessThan, lessThanOrEqualTo, log, matrixInverse, matrixMultiply, minus, minus, multiply, multiply, notEqualTo, observe, observe, plus, plus, pow, pow, reshape, reverseModeAutoDifferentiation, round, setAndCascade, setAndCascade, setValue, setValue, sigmoid, sin, slice, sum, take, tan, times, times, unaryMinus

Methods inherited from class io.improbable.keanu.vertices.Vertex

addChild, addParent, addParents, equals, eval, getChildren, getConnectedGraph, getId, getIndentation, getLabel, getObservedValue, getParents, getRawValue, getShape, getValue, hashCode, hasValue, isObserved, isProbabilistic, labeledAs, lazyEval, observe, observeOwnValue, sample, setAndCascade, setLabel, setParents, setParents, setValue, toString, unobserve

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface io.improbable.keanu.vertices.dbl.probabilistic.ProbabilisticDouble

dLogPdf, dLogPdf, dLogPdf, dLogPdf, dLogPdf, dLogPdf, logPdf, logPdf, logPdf

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

dLogProb, dLogProbAtValue, dLogProbAtValue, getValue, keepOnlyProbabilisticVertices, logProbAtValue, setValue

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

getObservedValue, isObserved, observableTypeFor, observe, unobserve

Methods inherited from interface io.improbable.keanu.vertices.dbl.Differentiable

getDualNumber

Constructor Detail

ChiSquaredVertex

public ChiSquaredVertex(int[] tensorShape,
                        IntegerVertex k)

One k that must match a proposed tensor shape of ChiSquared

If all provided parameters are scalar then the proposed shape determines the shape

Parameters:

tensorShape - the desired shape of the vertex

k - the number of degrees of freedom

ChiSquaredVertex

public ChiSquaredVertex(int[] tensorShape,
                        int k)

ChiSquaredVertex

public ChiSquaredVertex(IntegerTensor k)

One to one constructor for mapping some shape of k to a matching shaped ChiSquared.

Parameters:

k - the number of degrees of freedom

ChiSquaredVertex

public ChiSquaredVertex(int k)

Method Detail

sample

public DoubleTensor sample(KeanuRandom random)

Specified by:

sample in class Vertex<DoubleTensor>

Parameters:

random - source of randomness

Returns:

a sample from the vertex's distribution. For non-probabilistic vertices, this will always be the same value.

logProb

public double logProb(DoubleTensor value)

Description copied from interface: Probabilistic

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.

Specified by:

logProb in interface Probabilistic<DoubleTensor>

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

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

Description copied from interface: Probabilistic

The partial derivatives of the natural log prob.

Specified by:

dLogProb in interface Probabilistic<DoubleTensor>

Parameters:

value - 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).