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pmhtutorial (version 1.5)

Minimal Working Examples for Particle Metropolis-Hastings

Description

Routines for state estimate in a linear Gaussian state space model and a simple stochastic volatility model using particle filtering. Parameter inference is also carried out in these models using the particle Metropolis-Hastings algorithm that includes the particle filter to provided an unbiased estimator of the likelihood. This package is a collection of minimal working examples of these algorithms and is only meant for educational use and as a start for learning to them on your own.

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Install

install.packages('pmhtutorial')

Monthly Downloads

153

Version

1.5

License

GPL-2

Issues

1

Pull Requests

0

Stars

4

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0

Maintainer

Johan Dahlin

Last Published

March 22nd, 2019

Functions in pmhtutorial (1.5)

particleFilterSVmodel

Bootstrap particle filter for state estimate in a simple stochastic volatility model
kalmanFilter

Kalman filter for state estimate in a linear Gaussian state space model
example3_sv

Parameter estimation in a simple stochastic volatility model
makePlotsParticleMetropolisHastingsSVModel

Make plots for tutorial
example4_sv

Parameter estimation in a simple stochastic volatility model
particleMetropolisHastingsSVmodel

Particle Metropolis-Hastings algorithm for a stochastic volatility model model
particleMetropolisHastings

Particle Metropolis-Hastings algorithm for a linear Gaussian state space model
example5_sv

Parameter estimation in a simple stochastic volatility model
particleMetropolisHastingsSVmodelReparameterised

Particle Metropolis-Hastings algorithm for a stochastic volatility model model
particleFilter

Fully-adapted particle filter for state estimate in a linear Gaussian state space model
generateData

Generates data from a linear Gaussian state space model
example1_lgss

State estimation in a linear Gaussian state space model
example2_lgss

Parameter estimation in a linear Gaussian state space model