3 papers
stat.ME2021
Scalable Bayesian inference for time series via divide-and-conquer
Rihui Ou, Lachlan Astfalck, Deborshee Sen +1
Bayesian computational algorithms tend to scale poorly as data size increases. This has motivated divide-and-conquer-based approaches for scalable inference. These divide the data…
stat.ME2021
Bayesian inference on high-dimensional multivariate binary responses
Antik Chakraborty, Rihui Ou, David B. Dunson
It has become increasingly common to collect high-dimensional binary response data; for example, with the emergence of new sampling techniques in ecology. In smaller dimensions, mu…
stat.ML2018
Targeted stochastic gradient Markov chain Monte Carlo for hidden Markov models with rare latent states
Rihui Ou, Deborshee Sen, Alexander L Young +1
Markov chain Monte Carlo (MCMC) algorithms for hidden Markov models often rely on the forward-backward sampler. This makes them computationally slow as the length of the time serie…