Simulation.h File Reference

simulate(): a draw of a process model's next true state, the ground truth that a simulation runs a filter against and that nees() and NeesMonitor test its estimates by, and a draw of a measurement model's observation of a true state, which observe() turns into the measurement the filter takes, and a draw of a Markov chain's next value, the true mode of a switching or hidden Markov model (decisions.md [printing-and-simulation]). More...

#include <random>
#include <EmrysFilter/Core/Macros.h>
#include <EmrysFilter/Core/Multiply.h>
#include <EmrysFilter/Core/Time.h>
#include <EmrysFilter/Core/Weights.h>
#include <EmrysFilter/Distributions/Categorical.h>
#include <EmrysFilter/Distributions/ContinuousTimeMarkovChain.h>
#include <EmrysFilter/Distributions/Moments.h>
#include <EmrysFilter/Distributions/Sampling.h>
#include <EmrysFilter/Models/Measurement.h>
#include <EmrysFilter/Models/ProcessModel.h>
#include <EmrysFilter/Spaces/Space.h>

Go to the source code of this file.

Functions
template<Space Space_, typename Derived, ProcessModel Model, std::uniform_random_bit_generator G, typename... Args>
Moments< Space_, typenameDerived::Scalar >::VectorType	Emrys::simulate (Space_ const &space, Eigen::MatrixBase< Derived > const &x, Model const &model, G &rng, TimeStep< typename Derived::Scalar > auto const &dt, Args const &... args)
template<Space Space_, typename Derived, typename Model, std::uniform_random_bit_generator G, typename... Args> requires isFixedStep<Model>
Moments< Space_, typenameDerived::Scalar >::VectorType	Emrys::simulate (Space_ const &space, Eigen::MatrixBase< Derived > const &x, Model const &model, G &rng, Args const &... args)
template<Space Space_, typename Derived, typename Step, std::uniform_random_bit_generator G, typename... Args> requires isUnboundStep<Step>
Moments< Space_, typenameDerived::Scalar >::VectorType	Emrys::simulate (Space_ const &space, Eigen::MatrixBase< Derived > const &x, Step const &step, G &rng, Args const &... args)
template<Space Space_, typename Derived, MeasurementModel M, std::uniform_random_bit_generator G, typename... Args>
auto	Emrys::simulate (Space_ const &space, Eigen::MatrixBase< Derived > const &x, M const &model, G &rng, Args const &... args)
template<FixedSizeSpace Space_, typename Derived, typename Model, std::uniform_random_bit_generator G, typename... Inputs> requires (Space<Space_> && !isFixedStep<Model> && !isUnboundStep<Model>)
Moments< Space_, typenameDerived::Scalar >::VectorType	Emrys::simulate (Eigen::MatrixBase< Derived > const &x, Model const &model, G &rng, TimeStep< typename Derived::Scalar > auto const &dt, Inputs const &... inputs)
template<FixedSizeSpace Space_, typename Derived, typename Model, std::uniform_random_bit_generator G, typename... Inputs> requires (Space<Space_> && (isFixedStep<Model> || isUnboundStep<Model>))
Moments< Space_, typenameDerived::Scalar >::VectorType	Emrys::simulate (Eigen::MatrixBase< Derived > const &x, Model const &model, G &rng, Inputs const &... inputs)
template<FixedSizeSpace Space_, typename Derived, MeasurementModel M, std::uniform_random_bit_generator G, typename... Args> requires Space<Space_>
auto	Emrys::simulate (Eigen::MatrixBase< Derived > const &x, M const &model, G &rng, Args const &... args)
template<typename Space, typename Scalar, std::uniform_random_bit_generator G>
Eigen::Index	Emrys::simulate (Eigen::Index from, MarkovChain< Space, Scalar > const &chain, G &rng)
template<typename Space, typename Scalar, std::uniform_random_bit_generator G>
Eigen::Index	Emrys::simulate (Eigen::Index from, ContinuousTimeMarkovChain< Space, Scalar > const &chain, G &rng, TimeStep< Scalar > auto const &dt)

Detailed Description

simulate(): a draw of a process model's next true state, the ground truth that a simulation runs a filter against and that nees() and NeesMonitor test its estimates by, and a draw of a measurement model's observation of a true state, which observe() turns into the measurement the filter takes, and a draw of a Markov chain's next value, the true mode of a switching or hidden Markov model (decisions.md [printing-and-simulation]).

Read Consistency.h next.