223 citations · 435 across the 41 of their papers we have counts for
4 papers · 2 filters
A Geometric View of Posterior Approximation
Tian Chen, Jeffrey Streets, Babak Shahbaba
Although Bayesian methods are robust and principled, their application in practice could be limited since they typically rely on computationally intensive Markov Chain Monte Carlo…
Sampling constrained probability distributions using Spherical Augmentation
Shiwei Lan, Babak Shahbaba
Statistical models with constrained probability distributions are abundant in machine learning. Some examples include regression models with norm constraints (e.g., Lasso), probit,…
Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases
Cheng Zhang, Babak Shahbaba, Hongkai Zhao
For big data analysis, high computational cost for Bayesian methods often limits their applications in practice. In recent years, there have been many attempts to improve computati…
Precomputing Strategy for Hamiltonian Monte Carlo Method Based on Regularity in Parameter Space
Cheng Zhang, Babak Shahbaba, Hongkai Zhao
Markov Chain Monte Carlo (MCMC) algorithms play an important role in statistical inference problems dealing with intractable probability distributions. Recently, many MCMC algorith…