Bayesian Inference of State Space Models with the bssm Package

Efficient methods for Bayesian inference of state space models via particle Markov chain Monte Carlo and importance sampling type corrected Markov chain Monte Carlo. Currently supports models with Gaussian, Poisson, binomial, or negative binomial observation densities and Gaussian state dynamics, as well as general non-linear Gaussian models.

Funded by Academy of Finland grant 284513, "Exact approximate Monte Carlo methods for complex Bayesian inference".

Related Organizations

University of Jyväskylä
Finland

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average