# bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R

@article{Helske2021bssmBI, title={bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R}, author={Jouni Helske and Matti Vihola}, journal={R J.}, year={2021}, volume={13}, pages={471} }

We present an R package bssm for Bayesian non-linear/non-Gaussian state space modelling. Unlike the existing packages, bssm allows for easy-to-use approximate inference based on Gaussian approximations such as the Laplace approximation and the extended Kalman ﬁlter. The package accommodates also discretely observed latent diffusion processes. The inference is based on fully automatic, adaptive Markov chain Monte Carlo (MCMC) on the hyperparameters, with optional importance sampling post…

## 4 Citations

### Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo

- Computer ScienceScandinavian Journal of Statistics
- 2020

The IS approach provides a natural alternative to delayed acceptance (DA) pseudo‐marginal/particle MCMC, and has many advantages over DA, including a straightforward parallelization and additional flexibility in MCMC implementation, and is often competitive even without parallelization.

### ssMousetrack—Analysing Computerized Tracking Data via Bayesian State-Space Models in R

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Recent technological advances have provided new settings to enhance individual-based data collection and computerized-tracking data have became common in many behavioral and social research. By…

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Practical guidelines, recommendations, and software code for exploring and fitting dynamical systems models with linear and nonlinear change functions in the context of four illustrative examples are provided.

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- Computer ScienceModeling Earth Systems and Environment
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It is concluded that state space models perform better than long short-term memory networks with highly irregular and more complex surveillance data when health surveillance data are characterized by high fluctuations of the daily infection records and frequent occurrences of abrupt changes.

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