public class Geometric0UPM.M extends Discretes.M
mean
,
μ, is its one statistical-parameter. Also see the
UnParameterised MML.Geometric0
and class
Geometric0UPM
for estimation etc., and
[www].Model.Defaults
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
Modifier and Type | Field and Description |
---|---|
double |
mu
Statistical parameter μ, the mean, as a double.
|
Modifier and Type | Method and Description |
---|---|
double |
nlLH(Value ss)
Given sufficient statistics, ss = stats(ds), of a data-set ds,
return the negative log LikeliHood of ds.
|
double |
nlPr_n(int n)
The negative log probability of n; note, n≥0.
|
int |
random_n()
???random_n() could be made much(!) more efficient!!!
This implementation is just for completeness!
|
Value.Int |
random()
Return a random number from 'this' Geometric distribution;
calls
random_n() . |
nlPr, pr_n, pr, shifted
asEstimator, asUPModel, m1m2sp, msg, msg1, msg1bits, msg2, msg2bits, msgBits, nl2LH, nl2Pr, random, randomSeries, statParams, stats, stats, sumNlPr, type, zeroTriv
public M(double msg1, double msg2, Value mu)
public double nlPr_n(int n)
nlPr_n
in class Discretes.M
public double nlLH(Value ss)
public Value.Int random()
random_n()
.public int random_n()
random_n
in class Discretes.M