Bayesian Inference of 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, <10.1111>).
Gaussian, Poisson, binomial, negative binomial, and Gamma
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.10.1111>
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
- Due to the above change, argument
sim_states was removed from the Gaussian MCMC methods.
- MCMC functions are now less memory intensive, especially with
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)