ContinuousTimeMarkovChain.h File Reference

The continuous-time chain on a discrete space: ContinuousTimeMarkovChain, built from a generator of rates, from independent chains on the axes of a product, as persistent, or from mean dwell times and exit weights; its transition over an interval as a MarkovChain, through the matrix exponential; predict() over an interval; and stationary(), its stationary distribution (decisions.md [emissions]). More...

#include <tuple>
#include <type_traits>
#include <utility>
#include <unsupported/Eigen/MatrixFunctions>
#include <EmrysFilter/Core/Forwarding.h>
#include <EmrysFilter/Core/Macros.h>
#include <EmrysFilter/Core/Math.h>
#include <EmrysFilter/Core/Time.h>
#include <EmrysFilter/Distributions/Categorical.h>
#include <EmrysFilter/Spaces/Discrete.h>
#include <EmrysFilter/Spaces/Space.h>

Go to the source code of this file.

Classes
class	Emrys::ContinuousTimeMarkovChain< Space_, Scalar_ >
	A continuous-time Markov chain on a discrete space: the rate of moving from each value to each other value. More...

Functions
template<typename Scalar, typename Space>
CategoricalParams< Space, Scalar >	Emrys::predict (CategoricalParams< Space, Scalar > const &prior, ContinuousTimeMarkovChain< Space, Scalar > const &chain, TimeStep< Scalar > auto const &dt)
template<typename Scalar, typename Space>
std::expected< CategoricalParams< Space, Scalar >, ChainError >	Emrys::stationary (ContinuousTimeMarkovChain< Space, Scalar > const &chain)

Detailed Description

The continuous-time chain on a discrete space: ContinuousTimeMarkovChain, built from a generator of rates, from independent chains on the axes of a product, as persistent, or from mean dwell times and exit weights; its transition over an interval as a MarkovChain, through the matrix exponential; predict() over an interval; and stationary(), its stationary distribution (decisions.md [emissions]).

Models/Emission.h's ArrivalInterval observes a Markov-modulated Poisson process through it. Read Statistics.h next.