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Efficient methods for Bayesian inference of state space models via particle Markov chain Monte Carlo and parallel importance sampling type weighted Markov chain Monte Carlo (Vihola, Helske, and Franks, 2017, <arXiv:1609.02541>). Gaussian, Poisson, binomial, or negative binomial 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, Matti Vihola 
Maintainer  Jouni Helske <[email protected]> 
License  GPL (>= 2) 
Version  0.1.61 
Package repository  View on CRAN 
Installation 
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