mml

Class NormalUPM.M

java.lang.Object

la.la.Value

mml.Model

mml.UPModel.M

mml.ByPdf.M

mml.Continuous.M

mml.NormalUPM.M

All Implemented Interfaces:

java.lang.Comparable<Value>, HasPdf

Direct Known Subclasses:

NormalMu.M

Enclosing class:

NormalUPM

public class NormalUPM.M
extends Continuous.M

NormalUPM.M, a fully parameterised Normal Model (Gaussian probability distribution), N_μ,σ. The statistical parameter(s) is a pair, that is a 2-tuple, ⟨μ, σ⟩. Also see NormalMu.M for cases where μ is a given, and more on MML and the Normal: [www].

Nested Class Summary

Nested classes/interfaces inherited from class mml.Continuous.M

Continuous.M.Transform

Nested classes/interfaces inherited from class mml.Model

Model.Defaults

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	mu The mean, μ, and standard deviation, σ.
double	sigma The mean, μ, and standard deviation, σ.

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

Modifier	Constructor and Description
protected	M(double msg1, double msg2, double mu, Value sigma) A constructor with one statistical parameter, σ, for use by NormalMu.M.
	M(double msg1, double msg2, Value muSigma) Given the two-part message lengths, and statistical parameter (μ σ), construct Nμσ.

Method Summary

Modifier and Type	Method and Description
double	nlLH(Value ss) Given ss = stats(ds), of a data-set, ds, return nlLH(ss), that is the negative log likelihood of ds.
double	nlPdf_x(double x) The negative log pdf of a datum, x (double), for use by nlPdf.
double	random_x() Generate a double from Nμ,σ for use in random().
double	variance() Return ν = sigma2.

Methods inherited from class mml.Continuous.M

nlPdf, random_x, random, random, random, transform

Methods inherited from class mml.ByPdf.M

nlPr, nlPr, pdf

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, pr, 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

mu

public final double mu

The mean, μ, and standard deviation, σ.

sigma

public final double sigma

The mean, μ, and standard deviation, σ.

Constructor Detail

M

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

Given the two-part message lengths, and statistical parameter (μ σ), construct N_μσ.

M

protected M(double msg1,
            double msg2,
            double mu,
            Value sigma)

A constructor with one statistical parameter, σ, for use by NormalMu.M.

Method Detail

variance

public double variance()

Return ν = sigma².

nlPdf_x

public double nlPdf_x(double x)

The negative log pdf of a datum, x (double), for use by nlPdf.

Specified by:

nlPdf_x in class Continuous.M

nlLH

public double nlLH(Value ss)

Given ss = stats(ds), of a data-set, ds, return nlLH(ss), that is the negative log likelihood of ds.

Specified by:

nlLH in class Model

random_x

public double random_x()

Generate a double from N_μ,σ for use in random(). It calls Java's Random.nextGaussian() which "uses the polar method of G.E.P.Box, M.E.Muller and G.Marsaglia."

Overrides:

random_x in class Continuous.M