bssm {bssm}R Documentation

Bayesian Inference of State Space Models

Description

This package contains functions for efficient Bayesian inference of state space models, where model is assumed to be either

Details

* Exponential family state space models, where the state equation is linear Gaussian, and the conditional observation density is either Gaussian, Poisson, binomial, negative binomial or Gamma density.

* Basic stochastic volatility model.

* General non-linear model with Gaussian noise terms.

* Model with continuous SDE dynamics.

For formal definition of the currently supported models and methods, as well as some theory behind the IS-MCMC and psi-APF, see Helske and Vihola (2021), Vihola, Helske, Franks (2020) and the package vignettes.

References

Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R. ArXiv 2101.08492, <URL: https://arxiv.org/abs/2101.08492>.

Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492


[Package bssm version 1.1.7-1 Index]