99 citations · 314 across the 26 of their papers we have counts for
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stat.CO2015★ 12 cited
Optimal approximating Markov chains for Bayesian inference
James E. Johndrow, Jonathan C. Mattingly, Sayan Mukherjee +1
The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It is common to control computation time by making approximations to th…
stat.ME2015
Bayesian Approximate Kernel Regression with Variable Selection
Lorin Crawford, Kris C. Wood, Xiang Zhou +1
Nonlinear kernel regression models are often used in statistics and machine learning because they are more accurate than linear models. Variable selection for kernel regression mod…