- bayesbridge v0.1
Python package for Bayesian sparse regression based on the Bayesian bridge priors. The package implements the standard (Polya-Gamma augmented) Gibbs sampler as well as the CG-accelerated sampler of Nishimura & Suchard (2018). Source code available at my GitHub repository.
Hoseshoe scale sampler
Efficient rejection sampler for updating the local scale parameter in Gibbs sampling posterior distributions under (regularized) horseshoe models. Details and theoretical analysis can be found in the appendix of Nishimura and Suchard (2019).
Discontinuous Hamiltonian Monte Carlo
Python module implementing discontinuous Hamiltonian Monte Carlo of Nishimura et. al. (2017). Other codes used in the paper are also provided, including the modules to efficiently compute the log-likelihoods and their gradients of the Jolly-Seber and PAC Bayesian inference.
Probabilistic importance weighted matrix factorization
Python module for the Bayesian heteroscedastic matrix factorization model as described in Yang et. al. (2017).