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Efficient methods for Bayesian inference of state space models via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel importance sampling type weighted estimators (Vihola, Helske, and Franks, 2020, <arXiv:1609.02541>). Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and basic stochastic volatility models with Gaussian state dynamics, as well as general nonlinear Gaussian models and discretised diffusion models are supported.
Package details 


Author  Jouni Helske [aut, cre] (<https://orcid.org/000000017130793X>), Matti Vihola [aut] (<https://orcid.org/0000000280417222>) 
Maintainer  Jouni Helske <jouni.helske@iki.fi> 
License  GPL (>= 2) 
Version  1.0.0 
Package repository  View on CRAN 
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