mml

Class LinearD.Est

java.lang.Object

la.la.Value

la.la.Function

la.la.Function.Native

mml.Estimator

mml.UPModel.Est

mml.UPFunctionModel.Est

mml.LinearD.Est

All Implemented Interfaces:

java.lang.Comparable<Value>

Enclosing class:

LinearD

public class LinearD.Est
extends UPFunctionModel.Est

Estimator for a fully parameterised LinearD.M Linear-Model.

Nested Class Summary

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

Modifier and Type	Field and Description
protected double	kD latticeConstant(D).
protected double	R_s 1/σ "range", log(sigmaMax)-log(sigmaMin).
double	sigmaMax Bounds on σ; prior, pr(σ)~1/σ.
double	sigmaMin Bounds on σ; prior, pr(σ)~1/σ.

Fields inherited from class mml.Estimator

params

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
Est(Value ps) The estimators parameter 'ps' is the bounds on σ.

Method Summary

Modifier and Type	Method and Description
double	logF(double N, double sigma, Matrix xT_x) Log Fisher.
double	nlPrior(Vector a, double sigma) Negative log prior (probability density).
LinearD.M	ss2Model(Value ss) Given sufficient statistics ss=stats(ds) of a data-set 'ds', estimate a fully parameterised Linear Model.

Methods inherited from class mml.UPFunctionModel.Est

ds2FunctionModel, ds2Model, ss2FunctionModel

Methods inherited from class mml.UPModel.Est

defnParams, sp2Model, stats, stats, toString

Methods inherited from class mml.Estimator

apply, asUPModel, ds2Model, ds2ModelSp, ss2ModelSp, stats, stats

Methods inherited from class la.la.Function

bOp, fromBop, fromUop, main, 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

Field Detail

sigmaMin

public final double sigmaMin

Bounds on σ; prior, pr(σ)~1/σ.

sigmaMax

public final double sigmaMax

Bounds on σ; prior, pr(σ)~1/σ.

R_s

protected final double R_s

1/σ "range", log(sigmaMax)-log(sigmaMin).

kD

protected final double kD

latticeConstant(D).

Constructor Detail

Est

public Est(Value ps)

The estimators parameter 'ps' is the bounds on σ.

Method Detail

nlPrior

public double nlPrior(Vector a,
                      double sigma)

Negative log prior (probability density).

logF

public double logF(double N,
                   double sigma,
                   Matrix xT_x)

Log Fisher.

ss2Model

public LinearD.M ss2Model(Value ss)

Given sufficient statistics ss=stats(ds) of a data-set 'ds', estimate a fully parameterised Linear Model.

Specified by:

ss2Model in class UPFunctionModel.Est