mml

Class Continuous.M

java.lang.Object

la.la.Value

mml.Model

mml.UPModel.M

mml.ByPdf.M

mml.Continuous.M

All Implemented Interfaces:

java.lang.Comparable<Value>, HasPdf

Direct Known Subclasses:

BetaUPM.M, Continuous.Bounded.M, Continuous.M.Transform.MM, Continuous.Transform.M, ExponentialUPM.M, GammaUPM.M, HeavyTail.Over_x1.M, LaplaceUPM.M, NearInverse.M, NormalUPM.M

Enclosing class:

Continuous

public abstract class Continuous.M
extends ByPdf.M

The (abstract) class of fully parameterised Continuous Models. The UnParameterised Model is Continuous.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
class	Continuous.M.Transform A Continuous.M.Transform "is a" (extends) Continuous, i.e., is an UnParameterised Continuous model; the fully trivially parameterised Model is Continuous.M.Transform.MM.

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 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)

Method Summary

Modifier and Type	Method and Description
abstract double	nlPdf_x(double x) The negative log probability density of x; also see nlPdf(la.la.Value).
double	nlPdf(Value d) Calls nlPdf_x(d.x()).
double	random_x() Called by random(); this default is not implemented (throws an Exception) but see Continuous.Uniform.M.random_x() and NormalUPM.M.random_x(), say.
double[]	random_x(int n) Return an array double[n] of random_x().
Value.Real	random() Call random_x(), if implemented, to return an exact random Value.Real; it might be necessary to do something different if, for example, you want accuracy (AoM) to depend on the result's x().
Value.Cts	random(double AoM) Return a random {#link la.la.Value#cts Cts}(random_x(),AoM).
Vector	random(int n, double AoM) Return n random Cts each with the specified AoM.
Continuous.M	transform(Function.Cts2Cts f) This transform(f) is like Model.transform(f) except that, instead of "just" returning a Model, this transform returns a (trivially) parameterised Continuous.M (actually a Continuous.M.Transform.MM). Also see the related but different Continuous.transform(f).

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, nlLH, 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

Constructor Detail

M

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

Method Detail

nlPdf_x

public abstract double nlPdf_x(double x)

The negative log probability density of x; also see nlPdf(la.la.Value).

nlPdf

public double nlPdf(Value d)

Calls nlPdf_x(d.x()).

Specified by:

nlPdf in interface HasPdf

Specified by:

nlPdf in class ByPdf.M

random

public Value.Real random()

Call random_x(), if implemented, to return an exact random Value.Real; it might be necessary to do something different if, for example, you want accuracy (AoM) to depend on the result's x(). But if you want random Values with zero or other constant AoM, the present strategy is fine.

Overrides:

random in class Model

random

public Value.Cts random(double AoM)

Return a random {#link la.la.Value#cts Cts}(random_x(),AoM).

random_x

public double[] random_x(int n)

Return an array double[n] of random_x().

random

public Vector random(int n,
                     double AoM)

Return n random Cts each with the specified AoM.

random_x

public double random_x()

Called by random(); this default is not implemented (throws an Exception) but see Continuous.Uniform.M.random_x() and NormalUPM.M.random_x(), say. Note, there is an issue about generating random Cts Values with non-zero AoM if AoM depends on x.

transform

public Continuous.M transform(Function.Cts2Cts f)

This transform(f) is like Model.transform(f) except that, instead of "just" returning a Model, this transform returns a (trivially) parameterised Continuous.M (actually a Continuous.M.Transform.MM).
Also see the related but different Continuous.transform(f).