bssm: Bayesian Inference of State Space Models

In bssm: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

Bayesian Inference of State Space Models

Description

This package contains functions for efficient Bayesian inference of state space models (SSMs), where model is assumed to be either

Details

• Exponential family state space models, where the state equation is linear Gaussian, and the conditional observation density is either Gaussian, Poisson, binomial, negative binomial or Gamma density.

• Basic stochastic volatility model.

• General non-linear model with Gaussian noise terms.

• Model with continuous SDE dynamics.

Missing values in response series are allowed as per SSM theory and can be automatically predicted, but there can be no missing values in the system matrices of the model.

The bssm package includes several MCMC sampling and sequential Monte Carlo methods for models outside classic linear-Gaussian framework. For definitions of the currently supported models and methods, usage of the package as well as some theory behind the novel IS-MCMC and psi-APF algorithms, see Helske and Vihola (2021), Vihola, Helske, Franks (2020), and the package vignettes.

References

Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R. The R Journal (2021) 13:2, 578-589. https://doi.org/10.32614/RJ-2021-103

Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492

Examples

model <- bsm_lg(Nile,
  sd_y = tnormal(init = 100, mean = 100, sd = 100, min = 0),
  sd_level = tnormal(init = 50, mean = 50, sd = 100, min = 0),
  a1 = 1000, P1 = 500^2)

fit <- run_mcmc(model, iter = 2000)
fit

bssm documentation built on May 4, 2022, 1:06 a.m.