mml

Class LaplaceUPM

java.lang.Object

la.la.Value

la.la.Function

la.la.Function.Native

mml.UPModel

mml.ByPdf

mml.Continuous

mml.LaplaceUPM

All Implemented Interfaces:

java.lang.Comparable<Value>

public class LaplaceUPM
extends Continuous

The UnParameterised Laplace. Usually MML.Laplace suffices, but here is its class. Fully parameterised is Mμb. Also see the (negative-) Exponential.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
class	LaplaceUPM.M The fully parameterised Laplace probability distribution.

Nested classes/interfaces inherited from class mml.Continuous

Continuous.Bounded, Continuous.Transform, Continuous.Uniform

Nested classes/interfaces inherited from class mml.UPModel

UPModel.Est

Nested classes/interfaces inherited from class la.la.Function.Native

Function.Native.WithInverse

Nested classes/interfaces inherited from class la.la.Function

Function.Cts2Cts, Function.Cts2Cts2Cts, Function.CtsD2CtsD, Function.HasInverse, Function.Native, Function.Native2, Function.Native3

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.UPModel

dp

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
LaplaceUPM(Value dp) Requires definition parameters dp = triv.

Method Summary

Modifier and Type	Method and Description
UPModel.Est	estimator(Value ps) Parameters ps = (μmin, μmax, bmin, bmax).
LaplaceUPM.M	sp2Model(double msg1, double msg2, Value sp) Given two part message lengths, msg1 and msg2, and statistical parameters, sp, return an M.
Vector	stats(boolean add, Value ss0, Value ss1) Given sufficient statisticses 'ss0' and 'ss1', either 'add' them or remove (add=false) ss1 from ss0.
Value	stats(Vector ds, int lo, int hi) Given a data-set, ds, return statistics, ss = stats(ds,lo,hi), that is ds.slice[lo,hi).sorted(), a Sorted Slice.
java.lang.String	toString() Return a String representation of 'this' UnParameterised Model, including its problem-defining parameters.

Methods inherited from class mml.Continuous

transform

Methods inherited from class mml.UPModel

apply, defnParams, main, stats, stats, transform

Methods inherited from class la.la.Function

bOp, fromBop, fromUop, type, uOp

Methods inherited from class la.la.Value

AoM, bOp, compareTo, cons, cts, elt, errMsg, error, force, isTuple, just, n, nElts, nlAoM, pair, print, quad, real, RTE, triple, tuple, UOE, x

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

LaplaceUPM

public LaplaceUPM(Value dp)

Requires definition parameters dp = triv.

Method Detail

sp2Model

public LaplaceUPM.M sp2Model(double msg1,
                             double msg2,
                             Value sp)

Given two part message lengths, msg1 and msg2, and statistical parameters, sp, return an M.

Specified by:

sp2Model in class Continuous

stats

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

Given a data-set, ds, return statistics, ss = stats(ds,lo,hi), that is ds.slice[lo,hi).sorted(), a Sorted Slice. Can't think of anything better, not if you want the ability to add more elements of ds and update ss.

Specified by:

stats in class UPModel

stats

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

Given sufficient statisticses 'ss0' and 'ss1', either 'add' them or remove (add=false) ss1 from ss0. Also see {#link #stats(Vector,int,int) stats(ds,lo,hi)}.

Specified by:

stats in class UPModel

estimator

public UPModel.Est estimator(Value ps)

Parameters ps = (μ_min, μ_max, b_min, b_max). A uniform prior on μ in its range; log(b) uniform, b~1/b, in its range. Return an Estimator for a fully parameterised Laplace probability distribution.

Overrides:

estimator in class UPModel

toString

public java.lang.String toString()

Description copied from class: UPModel

Return a String representation of 'this' UnParameterised Model, including its problem-defining parameters.

Overrides:

toString in class UPModel