public static class HeavyTail.Over_x1 extends Continuous
HeavyTail.Over_x1.M and
its pdf is specified by pdf_x(x).| Modifier and Type | Class and Description |
|---|---|
class |
HeavyTail.Over_x1.M
|
Continuous.Bounded, Continuous.Transform, Continuous.UniformUPModel.EstFunction.Native.WithInverseFunction.Cts2Cts, Function.Cts2Cts2Cts, Function.CtsD2CtsD, Function.HasInverse, Function.Native, Function.Native2, Function.Native3Value.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| Constructor and Description |
|---|
Over_x1(Value t)
No non-trivial problem-defining parameters; t=triv.
|
| Modifier and Type | Method and Description |
|---|---|
HeavyTail.Over_x1.M |
sp2Model(double msg1,
double msg2,
Value sp)
Given two-part message lengths msg1 & msg2, and statistical
parameter sp, return a fully parameterised
M-Model. |
Vector |
stats(boolean add,
Value ss0,
Value ss1)
Combine sufficient statisticses 'ss0' and 'ss1' additively
(add=true), or remove ss1 from ss0 (add=false).
|
Vector |
stats(Vector ds,
int lo,
int hi)
Sufficient statistics, ss = stats(ds,lo,hi), of elements
[lo,hi) of a data-set ds; here ss = ds.[lo,hi).
|
transformapply, defnParams, estimator, main, stats, stats, toString, transformpublic Over_x1(Value t)
public Vector stats(Vector ds, int lo, int hi)
here.public Vector stats(boolean add, Value ss0, Value ss1)
UPModelstats(ds,lo,hi).public HeavyTail.Over_x1.M sp2Model(double msg1, double msg2, Value sp)
UPModelM-Model.
If 'this' UPModel produces a Model m, then
sp2Model(m.msg1(), m.msg2(), m.statParams())
must be equivalent to m. Also see apply(sp).sp2Model in class Continuous