mml

Class NearInverse.M

java.lang.Object

la.la.Value

mml.Model

mml.UPModel.M

mml.ByPdf.M

mml.Continuous.M

mml.NearInverse.M

All Implemented Interfaces:

java.lang.Comparable<Value>, HasPdf

Enclosing class:

NearInverse

public class NearInverse.M
extends Continuous.M

The fully parameterised NearInverse Model of positive reals. The "statistical parameter" is δ. Also see the UnParameterised model NearInverse.

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
protected Value.Cts	delta The "statistical parameter".
double	logIntegral The log of the normalising constant.

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 delta_x) A possible use is for σ's prior in MML.Normal.
M(double msg1, double msg2, Value delta) The standard constructor for NearInverse Model; statistical parameter delta must be a small Value.Cts in (0, 1) such as 0.1.

Method Summary

Modifier and Type	Method and Description
double	nlLH(Value ss) Assumes that statistics are the data-set itself.
double	nlPdf_x(double x) Nearly - log(1/x), that is + log(x), but not quite, corresponding to a pdf(x) of ~1/x, but not quite (as always, x > 0, of course).

Methods inherited from class mml.Continuous.M

nlPdf, random_x, 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, stats, stats, 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

delta

protected final Value.Cts delta

The "statistical parameter".

logIntegral

public final double logIntegral

The log of the normalising constant.

Constructor Detail

M

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

The standard constructor for NearInverse Model; statistical parameter delta must be a small Value.Cts in (0, 1) such as 0.1. The integral, ∫ = ∫_[0,1] {1/x^(1-δ)} + ∫_[1,∞] {1/x^(1+δ)} = { (x^δ) / δ}[0,1] + {-(x^-δ) / δ}[1,∞] = 2/δ gives the normalising constant.

M

public M(double delta_x)

A possible use is for σ's prior in MML.Normal.

Method Detail

nlPdf_x

public double nlPdf_x(double x)

Nearly - log(1/x), that is + log(x), but not quite, corresponding to a pdf(x) of ~1/x, but not quite (as always, x > 0, of course). Note, nlPdf(x) is continuous but its derivative has a jump at x = 1.

Specified by:

nlPdf_x in class Continuous.M

nlLH

public double nlLH(Value ss)

Assumes that statistics are the data-set itself.

Specified by:

nlLH in class Model