mml

Class R_D.Transform.M

java.lang.Object

la.la.Value

mml.Model

mml.UPModel.M

mml.ByPdf.M

mml.R_D.M

mml.R_D.Transform.M

All Implemented Interfaces:

java.lang.Comparable<Value>, HasPdf

Enclosing class:

R_D.Transform

public class R_D.Transform.M
extends R_D.M

The parameterised R_D.Transform.M Model, the UnParameterised model is R_D.Transform. Statistical parameters are as per the underlying R_D.M Model.
NB. The preferred way to transform an R_D is by using function R_D.transform(f).

Nested Class Summary

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

R_D.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
R_D.M	m The R_D.M Model 'm' doing the work behind the scenes.

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) Statistical parameter(s) 'sp' are as per the underlying R_D.M Model and are used to create 'm'.

Method Summary

Modifier and Type	Method and Description
double	nlLH(Value ss) m.nlLH(ss), but note that statistics 'ss' comes from stats.
double	nlPdf(Value xs) Negative log probability density as per m but "adjusted" by f's nlJ(xs).
double[]	random_x() Sample from m and apply f's inverse (provided it is defined).
Value	stats(boolean add, Value ss0, Value ss1) Combine statistics 'ss0' and 'ss1' as 'm' does.
Value	stats(Vector ds, int lo, int hi) m.stats(ds.map(f),...), i.e., transformed data.

Methods inherited from class mml.R_D.M

D, random, random, transform

Methods inherited from class mml.ByPdf.M

nlPr, nlPr, pdf

Methods inherited from class mml.UPModel.M

asGiven, asGiven, 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

m

public final R_D.M m

The R_D.M Model 'm' doing the work behind the scenes.

Constructor Detail

M

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

Statistical parameter(s) 'sp' are as per the underlying R_D.M Model and are used to create 'm'.

Method Detail

nlPdf

public double nlPdf(Value xs)

Negative log probability density as per m but "adjusted" by f's nlJ(xs).

Specified by:

nlPdf in interface HasPdf

Specified by:

nlPdf in class ByPdf.M

nlLH

public double nlLH(Value ss)

m.nlLH(ss), but note that statistics 'ss' comes from stats.

Specified by:

nlLH in class Model

stats

public Value stats(Vector ds,
                   int lo,
                   int hi)

m.stats(ds.map(f),...), i.e., transformed data. Also see nlLH(ss).

Overrides:

stats in class UPModel.M

stats

public Value stats(boolean add,
                   Value ss0,
                   Value ss1)

Combine statistics 'ss0' and 'ss1' as 'm' does.

Overrides:

stats in class UPModel.M

random_x

public double[] random_x()

Sample from m and apply f's inverse (provided it is defined).

Overrides:

random_x in class R_D.M