GitHub page.
Julia
BNP_WMReg_Joint.jl provides implementation of a nonparametric mixture of autoregressive models with lag selection. See the article for details. Code for fitting and assessing the models is found in BNP_WMAR_examples.
References
Bayesian Nonparametric Density Autoregression with Lag Selection,
Heiner, M. and Kottas, A. (2020+), arXiv preprint arXiv:.
(link to paper)
Julia
MTD.jl provides a Bayesian implementation of the MTD model, along with our extension for model selection. See the article for details. Code for fitting and assessing the models is found in MTD_examples.
References
Estimation and selection for high-order Markov chains with Bayesian mixture transition distribution models,
Heiner, M. and Kottas, A. (2019+), arXiv preprint arXiv:1906.10781.
(link to paper)
Julia
SparseProbVec provides functions to sample the sparse Dirichlet mixture and stick-breaking mixture distributions for probability vectors. See the article for details.
R
SparseProbVec is an R package. See the test script and function documentation for usage examples.
References
Structured priors for sparse probability vectors with application to model selection in Markov chains,
Heiner, M., Kottas, A., and Munch, S. (2019), Statistics and Computing, 29.
(link to paper)