Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

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, ). 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.


News

bssm 0.1.4 (Release date: 2018-02-04)

  • MCMC output can now be defined with argument type. Instead of returning joint posterior samples, run_mcmc can now return only marginal samples of theta, or summary statistics of the states.
  • Due to the above change, argument sim_states was removed from the Gaussian MCMC methods.
  • MCMC functions are now less memory intensive, especially with type="theta".

bssm 0.1.3 (Release date: 2018-01-07)

  • Streamlined the output of the print method for MCMC results.
  • Fixed major bugs in predict method which caused wrong values for the prediction intervals.
  • Fixed some package dependencies.
  • Sampling for standard deviation parameters of BSM and their non-Gaussian counterparts is now done in logarithmic scale for slightly increased efficiency.
  • Added a new model class ar1 for univariate (possibly noisy) Gaussian AR(1) processes.
  • MCMC output now includes posterior predictive distribution of states for one step ahead to the future.

bssm 0.1.2 (Release date: 2017-11-21)

  • API change for run_mcmc: All MCMC methods are now under the argument method, instead of having separate arguments for delayed acceptance and IS schemes.
  • summary method for MCMC output now omits the computation of SE and ESS in order to speed up the function.
  • Added new model class lgg_ssm, which is a linear-Gaussian model defined directly via C++ like non-linear nlg_ssm models. This allows more flexible prior definitions and complex system matrix constructions.
  • Added another new model class, sde_ssm, which is a model with continuous state dynamics defined as SDE. These too are defined via couple simple C++ functions.
  • Added non-gaussian AR(1) model class.
  • Added argument nsim for predict method, which allows multiple draws per MCMC iteration.
  • The noise multiplier matrices H and R in nlg_ssm models can now depend on states.

bssm 0.1.1-1 (Release date: 2017-06-27)

  • Use byte compiler.
  • Skip tests relying in certain numerical precision on CRAN.

bssm 0.1.1 (Release date: 2017-06-27)

  • Switched from C++11 PRNGs to sitmo.
  • Fixed some portability issues in C++ codes.

bssm 0.1.0 (Release date: 2017-06-24)

  • Initial release.

Reference manual

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install.packages("bssm")

0.1.5 by Jouni Helske, a month ago


Report a bug at https://github.com/helske/bssm/issues


Browse source code at https://github.com/cran/bssm


Authors: Jouni Helske, Matti Vihola


Documentation:   PDF Manual  


Task views: Time Series Analysis


GPL (>= 2) license


Imports coda, diagis, ggplot2, Rcpp

Suggests bayesplot, KFAS, knitr, MASS, ramcmc, rmarkdown, sde, sitmo, testthat

Linking to BH, Rcpp, RcppArmadillo, ramcmc, sitmo

System requirements: C++11


See at CRAN