ssmkit::process Namespace Reference

Stochastic Processes. More...

Classes
class	BaseProcess
class	Hierarchical
	A stochastic process constructed as hierarchy of stochastic processes. More...
class	Markov
	A first-order Markov process. More...
class	Memoryless
	A Memoryless (independent/white) random process. More...

Functions
template<class... TArgs>
Hierarchical< TArgs...>	makeHierarchical (TArgs...args)
	A convenient builder for Hierarchical process. More...
template<typename TPDF , typename TParamMap , typename TInitialPDF >
Markov< TPDF, TParamMap, TInitialPDF >	makeMarkov (distribution::Conditional< TPDF, TParamMap > cpdf, TInitialPDF init_pdf)
	Convenient builder for Markov process. More...
template<typename TPDF , typename TParamMap >
Memoryless< TPDF, TParamMap >	makeMemoryless (distribution::Conditional< TPDF, TParamMap > cpdf)
	Convenient builder for Memoryless process. More...

Detailed Description

Stochastic Processes.

Function Documentation

Hierarchical<TArgs...> ssmkit::process::makeHierarchical

(

TArgs...

args

)

A convenient builder for Hierarchical process.

use this for template argument deduction.

Parameters

args	... Process layers, see Hierarchical::Hierarchical

Template Parameters

TArgs

... Type of the process layers

Returns

Hierarchical process object build from args...

Precondition

See Hierarchical::Hierarchical

Definition at line 151 of file hierarchical.hpp.

Markov<TPDF, TParamMap, TInitialPDF> ssmkit::process::makeMarkov	(	distribution::Conditional< TPDF, TParamMap >	cpdf,
		TInitialPDF	init_pdf
	)

Convenient builder for Markov process.

Use this when you want to use type deduction

Returns

Markov process object

Parameters

init_pdf	Initial probability distribution \(p(\mathbf{x}_0)\)
cpdf	distribution::Conditional PDF characterizing inter time-slice dependency \(p(\mathbf{x}_k|\mathbf{x}_{k-1}, y^1_k, \cdots, y^N_k)\)

Precondition

init_pdf should provide random and likelihood methods.

The type of the random variable and the first condition variable of cpdf should be the same. The cpdf should have at least one condition variable.

Definition at line 123 of file markov.hpp.

Memoryless<TPDF, TParamMap> ssmkit::process::makeMemoryless

(

distribution::Conditional< TPDF, TParamMap >

cpdf

)

Convenient builder for Memoryless process.

Use this when you want to use type deduction

Returns

Memoryless process object

Parameters

cpdf	distribution::Conditional PDF defining the process \(p(\mathbf{x}_k| y^0_k, \cdots, y^N_k)\)

Definition at line 104 of file memoryless.hpp.