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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 non-linear Gaussian models and discretised diffusion models are supported.
Package details |
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Author | Jouni Helske, Matti Vihola |
Maintainer | Jouni Helske <[email protected]> |
License | GPL (>= 2) |
Version | 0.1.6-1 |
Package repository | View on CRAN |
Installation |
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