mml

Class Poisson0UPM.M

java.lang.Object

la.la.Value

mml.Model

mml.UPModel.M

mml.Discretes.M

mml.Poisson0UPM.M

All Implemented Interfaces:

java.lang.Comparable<Value>

Enclosing class:

Poisson0UPM

public class Poisson0UPM.M
extends Discretes.M

The fully parameterised Poisson0 Model (probability distribution) on integers in [0, ∞); its one statistical parameter, α, is both the mean and variance. Also see the UnParameterised MML.Poisson0, and [www].

Nested Class Summary

Nested classes/interfaces inherited from class mml.Model

Model.Defaults, Model.Transform

Nested classes/interfaces inherited from class la.la.Value

Value.Atomic, Value.Bool, Value.Char, Value.Chars, Value.Cts, Value.Defer, Value.Discrete, Value.Enum, Value.Inc_Or, Value.Int, Value.Lambda, Value.List, Value.Maybe, Value.Option, Value.Real, Value.Scannable, Value.Structured, Value.Triv, Value.Tuple

Field Summary

Fields

Modifier and Type	Field and Description
double	alpha The mean (and variance) of the Poisson0 distribution.

Fields inherited from class mml.Model

msg1, msg2, sp

Fields inherited from class la.la.Value

C, comparator, CR, E, eight, ffalse, five, fiveR, four, fourR, half, negCR, negOne, negOneR, negTenR, nilVal, nine, None, one, oneR, PI, PIby2, point01, point1, point5, point9, quarter, seven, six, ten, tenR, three, threeR, triv, ttrue, two, twoR, zero, zeroR

Constructor Summary

Constructors

Constructor and Description
M(double msg1, double msg2, Value alpha)

Method Summary

Modifier and Type	Method and Description
double	nlLH(Value ss) Given sufficient statistics, ss = stats(ds), of a data-set ds, return the negative log LikeliHood of ds.
double	nlPr_n(int n) The negative log of pr(n|α) = (e-α).(αn) / n!, integer n ≥ 0.
int	random_n() ??? Poisson0.random_n() could be made much(!) more efficient!! This implementation is just for completeness.
Value.Int	random() Return a random number from 'this' Poisson distribution; calls random_n().

Methods inherited from class mml.Discretes.M

nlPr, pr_n, pr, shifted

Methods inherited from class mml.UPModel.M

asGiven, asGiven, stats, stats, toString

Methods inherited from class mml.Model

asEstimator, asUPModel, m1m2sp, msg, msg1, msg1bits, msg2, msg2bits, msgBits, nl2LH, nl2Pr, random, randomSeries, statParams, stats, stats, sumNlPr, transform, type, zeroTriv

Methods inherited from class la.la.Value

AoM, apply, bOp, compareTo, cons, cts, elt, errMsg, error, force, isTuple, just, main, n, nElts, nlAoM, pair, print, quad, real, RTE, triple, tuple, UOE, uOp, x

Methods inherited from class java.lang.Object

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

Field Detail

alpha

public final double alpha

The mean (and variance) of the Poisson0 distribution.

Constructor Detail

M

public M(double msg1,
         double msg2,
         Value alpha)

Method Detail

nlPr_n

public double nlPr_n(int n)

The negative log of pr(n|α) = (e^-α).(αⁿ) / n!, integer n ≥ 0.

Specified by:

nlPr_n in class Discretes.M

nlLH

public double nlLH(Value ss)

Given sufficient statistics, ss = stats(ds), of a data-set ds, return the negative log LikeliHood of ds.

Specified by:

nlLH in class Model

random

public Value.Int random()

Return a random number from 'this' Poisson distribution; calls random_n().

Overrides:

random in class Model

random_n

public int random_n()

??? Poisson0.random_n() could be made much(!) more efficient!! This implementation is just for completeness.

Specified by:

random_n in class Discretes.M