1 citations · 2 across the 3 of their papers we have counts for
3 papers
Accelerating Metropolis-Hastings with Lightweight Inference Compilation
Feynman Liang, Nimar Arora, Nazanin Tehrani +3
In order to construct accurate proposers for Metropolis-Hastings Markov Chain Monte Carlo, we integrate ideas from probabilistic graphical models and neural networks in an open-sou…
PPL Bench: Evaluation Framework For Probabilistic Programming Languages
Sourabh Kulkarni, Kinjal Divesh Shah, Nimar Arora +9
We introduce PPL Bench, a new benchmark for evaluating Probabilistic Programming Languages (PPLs) on a variety of statistical models. The benchmark includes data generation and eva…
Newtonian Monte Carlo: single-site MCMC meets second-order gradient methods
Nimar S. Arora, Nazanin Khosravani Tehrani, Kinjal Divesh Shah +7
Single-site Markov Chain Monte Carlo (MCMC) is a variant of MCMC in which a single coordinate in the state space is modified in each step. Structured relational models are a good c…