mml

Class LinearD.M

java.lang.Object

la.la.Value

mml.Model

mml.FunctionModel

mml.UPFunctionModel.M

mml.LinearD.M

All Implemented Interfaces:

java.lang.Comparable<Value>, HasPdf

Enclosing class:

LinearD

public class LinearD.M
extends UPFunctionModel.M
implements HasPdf

The fully parameterised function-model; LinearD is the UnParameterised function-model. Also see Linear1.M.

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[]	a Statistical parameter, 'a[D+1]' of the LinearD Model y=a·x+b+N(0,σ) where 'b' is a[D].
double	sigma Statistical parameter of the Model y=a·x+b+N(0,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

Constructor and Description
M(double msg1, double msg2, Value sp) Two part message lengths, msg1 and msg2, and statistical parameters sp.

Method Summary

Modifier and Type	Method and Description
NormalUPM.M	condModel(Value x) Conditional Model of output datum y given input datum x, i.e., the Normal Model, N(a·x+b,σ).
double	nlLH(Value ss) Given sufficient statistics, ss=stats(ds), of a data-set ds, return the negative log LikeliHood of ds.
double	nlPdf(Value xy) Negative log pdf of 'y' given 'x'.
double	pdf(Value xy) pdf of 'y' given 'x'.

Methods inherited from class mml.UPFunctionModel.M

asGiven, asGiven, stats, stats, toString

Methods inherited from class mml.FunctionModel

asUPModel, condNl2Pr, condNlPr, condPr, nlPr, pr, random, random

Methods inherited from class mml.Model

asEstimator, 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

a

public final double[] a

Statistical parameter, 'a[D+1]' of the LinearD Model y=a·x+b+N(0,σ) where 'b' is a[D]. (The maths is easier if we define X₊ to be X with an extra column of 1s, and 'b' becomes a[D].)

sigma

public final double sigma

Statistical parameter of the Model y=a·x+b+N(0,σ).

Constructor Detail

M

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

Two part message lengths, msg1 and msg2, and statistical parameters sp.

Method Detail

condModel

public NormalUPM.M condModel(Value x)

Conditional Model of output datum y given input datum x, i.e., the Normal Model, N(a·x+b,σ).

Specified by:

condModel in class FunctionModel

nlPdf

public double nlPdf(Value xy)

Negative log pdf of 'y' given 'x'.

Specified by:

nlPdf in interface HasPdf

pdf

public double pdf(Value xy)

pdf of 'y' given 'x'.

Specified by:

pdf in interface HasPdf

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