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Efficient methods for Bayesian inference of state space models via Markov chain Monte Carlo (MCMC) based on parallel importance sampling type weighted estimators (Vihola, Helske, and Franks, 2020, doi:10.1111/sjos.12492), particle MCMC, and its delayed acceptance version. Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and basic stochastic volatility models with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretised diffusion models are supported. See Helske and Vihola (2021, doi:10.32614/RJ-2021-103) for details.
| Citation | bssm citation info |
| github.com/helske/bssm | |
| System requirements | pandoc (>= 1.12.3, needed for vignettes) |
| Bug report | File report |
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