public class Poisson0UPM.M extends Discretes.M
α
, is both the mean and variance.
Also see the UnParameterised MML.Poisson0
, 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 |
alpha
The mean (and variance) of the Poisson0 distribution.
|
Constructor and Description |
---|
M(double msg1,
double msg2,
Value alpha) |
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 of
|
int |
random_n()
??? Poisson0.random_n() could be made much(!) more efficient!!
This implementation is just for completeness.
|
Value.Int |
random()
Return a random number from 'this' Poisson 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 alpha)
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