io.improbable.keanu.vertices.dbl.probabilistic

Class StudentTVertex

java.lang.Object

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

io.improbable.keanu.vertices.dbl.DoubleVertex

io.improbable.keanu.vertices.dbl.probabilistic.StudentTVertex

All Implemented Interfaces:

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

public class StudentTVertex
extends DoubleVertex
implements ProbabilisticDouble

Field Summary

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

ID_GENERATOR

Constructor Summary

Constructors

Constructor and Description
StudentTVertex(int v)
StudentTVertex(int[] tensorShape, int v)
StudentTVertex(int[] tensorShape, IntegerVertex v) One v that must match a proposed tensor shape of StudentT
StudentTVertex(IntegerVertex v)

Method Summary

Modifier and Type	Method and Description
java.util.Map<java.lang.Long,DoubleTensor>	dLogProb(DoubleTensor t) The partial derivatives of the natural log prob.
IntegerVertex	getV()
double	logProb(DoubleTensor t) 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, cos, div, div, divideBy, divideBy, exp, floor, getValue, lambda, log, matrixMultiply, minus, minus, multiply, multiply, observe, observe, plus, plus, pow, pow, 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, getParents, getRawValue, getShape, getValue, hashCode, hasValue, isObserved, isProbabilistic, lazyEval, observe, observeOwnValue, sample, setAndCascade, setParents, setParents, setValue, unobserve, updateValue

Methods inherited from class java.lang.Object

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

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

dLogPdf, dLogPdf, dLogPdf, logPdf, logPdf, logPdf

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

dLogProbAtValue, getValue, keepOnlyProbabilisticVertices, logProbAtValue, setValue

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

isObserved, observableTypeFor, observe, unobserve

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

getDualNumber, keepOnlyDifferentiableVertices

Constructor Detail

StudentTVertex

public StudentTVertex(int[] tensorShape,
                      IntegerVertex v)

One v that must match a proposed tensor shape of StudentT

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

Parameters:

tensorShape - expected tensor shape

v - Degrees of Freedom

StudentTVertex

public StudentTVertex(int[] tensorShape,
                      int v)

StudentTVertex

public StudentTVertex(IntegerVertex v)

StudentTVertex

public StudentTVertex(int v)

Method Detail

getV

public IntegerVertex getV()

logProb

public double logProb(DoubleTensor t)

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:

t - 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<java.lang.Long,DoubleTensor> dLogProb(DoubleTensor t)

Description copied from interface: Probabilistic

The partial derivatives of the natural log prob.

Specified by:

dLogProb in interface Probabilistic<DoubleTensor>

Parameters:

t - at a given value

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).

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.