mml

Class Linear1.M

java.lang.Object

la.la.Value

mml.Model

mml.FunctionModel

mml.UPFunctionModel.M

mml.Linear1.M

All Implemented Interfaces:

java.lang.Comparable<Value>, HasPdf

Enclosing class:

Linear1

public class Linear1.M
extends UPFunctionModel.M
implements HasPdf

A fully parameterised Linear1 Model, y = a * x + b + N(0, σ) . Also see the UnParameterised Linear1 Model.

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 parameters of the Model y = a × x + b + N(0, σ).
double	b Statistical parameters of the Model y = a × x + b + N(0, σ).
double	sigma Statistical parameters 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 sps) Two-part message lengths msg1 and msg2, and statistical parameters sps = ⟨a, b, σ⟩.

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, N(ax + b, σ) .
double	nlLH(Value ss) Given a data-set, ds = zip xs ys, having sufficient statistics ss = stats(ds), return the negative log likelihood of the ys given the xs.
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 parameters of the Model y = a × x + b + N(0, σ).

b

public final double b

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

sigma

public final double sigma

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

Constructor Detail

M

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

Two-part message lengths msg1 and msg2, and statistical parameters sps = ⟨a, b, σ⟩.

Method Detail

condModel

public NormalUPM.M condModel(Value x)

Conditional Model of output datum y given input datum x, i.e., the Normal, N(ax + 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 a data-set, ds = zip xs ys, having sufficient statistics ss = stats(ds), return the negative log likelihood of the ys given the xs. (Recall that the input datum (variable), x, is common knowledge.)

Specified by:

nlLH in class Model