walker: Bayesian Generalized Linear Models with Time-Varying Coefficients

By Jouni Helske in R Package State Space Models Bayesian Inference

June 1, 2021

Bayesian generalized linear models with time-varying coefficients as in Helske (2020, arXiv:2009.07063). Gaussian, Poisson, and binomial observations are supported. The Markov chain Monte Carlo (MCMC) computations are done using Hamiltonian Monte Carlo provided by Stan, using a state space representation of the model in order to marginalise over the coefficients for efficient sampling. For non-Gaussian models, the package uses the importance sampling type estimators based on approximate marginal MCMC as in Vihola, Helske, Franks (2020, doi:10.1111/sjos.12492).

Posted on:
June 1, 2021
1 minute read, 76 words
R Package State Space Models Bayesian Inference
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