R package pmhtutorial: Minimal Working Examples for Particle Metropolis-Hastings. 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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References in zbMATH (referenced in 1 article , 1 standard article )
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- Johan Dahlin, Thomas B. Schön: Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models (2019) not zbMATH