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 (MCMC) and MCMC based on parallel importance sampling type weighted estimators (Vihola, Helske, and Franks, 2020, ). Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and basic stochastic volatility models with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretised diffusion models are supported.

News

bssm 0.1.7 (Release date: 2019-03-19)

Fixed a bug in EKF smoother which resulted wrong smoothed state estimates in case of partially missing multivariate observations. Thanks for Santeri Karppinen for spotting the bug.
Added twisted SMC based simulation smoothing algorithm for Gaussian models, as an alternative to Kalman smoother based simulation.

bssm 0.1.6-1 (Release date: 2018-11-20)

Fixed wrong dimension declarations in pseudo-marginal MCMC and logLik methods for SDE and ng_ar1 models.
Added a missing Jacobian for ng_bsm and bsm models using IS-correction.
Changed internal parameterization of ng_bsm and bsm models from log(1+theta) to log(theta).

bssm 0.1.5 (Release date: 2018-05-23)

Fixed the Cholesky decomposition in filtering recursions of multivariate models.
as_gssm now works for multivariate Gaussian models of KFAS as well.
Fixed several issues regarding partially missing observations in multivariate models.
Added the MASS package to Suggests as it is used in some unit tests.
Added missing type argument to SDE MCMC call with delayed acceptance.

bssm 0.1.4-1 (Release date: 2018-02-04)

Fixed the use of uninitialized values in psi-filter from version 0.1.3.

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.