21 citations · 70 across the 8 of their papers we have counts for
Showing cs.AIShow all
2 papers · 1 filter
cs.AI2015★ 1 cited
Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs
David Tolpin, Jan Willem van de Meent, Brooks Paige +1
We introduce an adaptive output-sensitive Metropolis-Hastings algorithm for probabilistic models expressed as programs, Adaptive Lightweight Metropolis-Hastings (AdLMH). The algori…
cs.AI2014★ 7 cited
Learning Probabilistic Programs
Yura N. Perov, Frank D. Wood
We develop a technique for generalising from data in which models are samplers represented as program text. We establish encouraging empirical results that suggest that Markov chai…