bssm: Bayesian Inference of State Space Models

Description References


This package contains functions for Bayesian inference of basic stochastic volatility model and exponential family state space models, where the state equation is linear and Gaussian, and the conditional observation density is either Gaussian, Poisson, binomial, negative binomial or Gamma density. General non-linear Gaussian models and models with continuous SDE dynamics are also supported. For formal definition of the currently supported models and methods, as well as some theory behind the IS-MCMC and psi-APF, see the package vignettes and Vihola, Helske, Franks (2020).


Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 2020; 1– 38.

bssm documentation built on July 10, 2021, 9:07 a.m.