65 citations · 138 across the 3 of their papers we have counts for
3 papers
NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
Matthew Hoffman, Pavel Sountsov, Joshua V. Dillon +3
Hamiltonian Monte Carlo is a powerful algorithm for sampling from difficult-to-normalize posterior distributions. However, when the geometry of the posterior is unfavorable, it may…
Stochastic gradient descent methods for estimation with large data sets
Dustin Tran, Panos Toulis, Edoardo M. Airoldi
We develop methods for parameter estimation in settings with large-scale data sets, where traditional methods are no longer tenable. Our methods rely on stochastic approximations,…
Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
Aki Vehtari, Andrew Gelman, Tuomas Sivula +7
A common divide-and-conquer approach for Bayesian computation with big data is to partition the data, perform local inference for each piece separately, and combine the results to…