Measure Transformer Semantics for Bayesian Machine Learning

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Measure Transformer Semantics for Bayesian Machine Learning

3 August 2013

Andrew D. Gordon

Michael Greenberg

Jurgen Van Gael

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Papers citing "Measure Transformer Semantics for Bayesian Machine Learning"

9 / 9 papers shown

Title
Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives J. Warrell M. Gerstein GP 49 1 0 30 Mar 2022
Mixed Nondeterministic-Probabilistic Automata: Blending graphical probabilistic models with nondeterminism A. Benveniste Jean-Baptiste Raclet TPM 52 1 0 19 Jan 2022
Applied Measure Theory for Probabilistic Modeling Chad Scherrer Moritz Schauer 35 1 0 01 Oct 2021
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently Tim Reichelt Adam Goliñski C.-H. Luke Ong Tom Rainforth TPM 60 0 0 09 Jun 2021
Learning Proposals for Probabilistic Programs with Inference Combinators Sam Stites Heiko Zimmermann Hao Wu Eli Sennesh Jan-Willem van de Meent NAI 84 16 0 01 Mar 2021
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and Deterministic Maria I. Gorinova Andrew D. Gordon Charles Sutton 77 24 0 02 Nov 2018
An Introduction to Probabilistic Programming Jan-Willem van de Meent Brooks Paige Hongseok Yang Frank Wood GP 88 200 0 27 Sep 2018
Deriving Probability Density Functions from Probabilistic Functional Programs Sooraj Bhat J. Borgström Andrew D. Gordon Claudio V. Russo 50 46 0 04 Apr 2017
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints S. Staton Hongseok Yang C. Heunen Ohad Kammar Frank Wood 89 136 0 19 Jan 2016