mml

Class Continuous.Transform.M

java.lang.Object

la.la.Value

mml.Model

mml.UPModel.M

mml.ByPdf.M

mml.Continuous.M

mml.Continuous.Transform.M

All Implemented Interfaces:

java.lang.Comparable<Value>, HasPdf

Enclosing class:

Continuous.Transform

public class Continuous.Transform.M
extends Continuous.M

The fully parameterised transformed Model; the UnParameterised model is Continuous.Transform. Statistical parameters are as per the enclosing Continuous. Note that a UPModel.Transform.M is just a UPModel.M but a Continuous.Transform.M is a (extends) Continuous.M. Also see the related but different Continuous.M.Transform.MM.
NB. The preferred way to transform a Continuous is to use function transform(f).

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
Continuous.M	m The Continuous.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 Continuous.M 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_x(double x) Negative log probability density as per m but corrected by f's derivative.
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.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, 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 Continuous.M m

The Continuous.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 Continuous.M and are used to create 'm'.

Method Detail

nlPdf_x

public double nlPdf_x(double x)

Negative log probability density as per m but corrected by f's derivative.

Specified by:

nlPdf_x in class Continuous.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 Continuous.M