140 citations · 735 across the 46 of their papers we have counts for
9 papers · 1 filter
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
Mario Lezcano Casado, Atilim Gunes Baydin, David Martinez Rubio +8
We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data. In scientific fields ranging from po…
Inference Compilation and Universal Probabilistic Programming
Tuan Anh Le, Atilim Gunes Baydin, Frank Wood
We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establish…
Spreadsheet Probabilistic Programming
Mike Wu, Yura Perov, Frank Wood +1
Spreadsheet workbook contents are simple programs. Because of this, probabilistic programming techniques can be used to perform Bayesian inversion of spreadsheet computations. What…
Data-driven Sequential Monte Carlo in Probabilistic Programming
Yura N Perov, Tuan Anh Le, Frank Wood
Most of Markov Chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) algorithms in existing probabilistic programming systems suboptimally use only model priors as proposal dis…
Maximum a Posteriori Estimation by Search in Probabilistic Programs
David Tolpin, Frank Wood
We introduce an approximate search algorithm for fast maximum a posteriori probability estimation in probabilistic programs, which we call Bayesian ascent Monte Carlo (BaMC). Proba…
Path Finding under Uncertainty through Probabilistic Inference
David Tolpin, Brooks Paige, Jan Willem van de Meent +1
We introduce a new approach to solving path-finding problems under uncertainty by representing them as probabilistic models and applying domain-independent inference algorithms to…
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…
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…
A Compilation Target for Probabilistic Programming Languages
Brooks Paige, Frank Wood
Forward inference techniques such as sequential Monte Carlo and particle Markov chain Monte Carlo for probabilistic programming can be implemented in any programming language by cr…