International audienceA streaming probabilistic program receives a stream of observations and produces a stream of distributions that are conditioned on these observations. Efficient inference is often possible in a streaming context using Rao-Blackwellized particle filters (RBPFs), which exactly solve inference problems when possible and fall back on sampling approximations when necessary. While RBPFs can be implemented by hand to provide efficient inference, the goal of streaming probabilistic programming is to automatically generate such efficient inference implementations given input probabilistic programs. In this work, we propose semi-symbolic inference, a technique for executing probabilistic programs using a runtime inference system...
Probabilistic programming is becoming an attractive approach to probabilistic machine learning. Thro...
Synchronous reactive languages were introduced for designing and implementing real-time control soft...
In order to handle real-world problems, state-of-the-art probabilistic logic and learning frameworks...
Efficient inference is often possible in a streaming context using Rao-Blackwellized particle filter...
Probabilistic programming languages aid developers performing Bayesian in...
© 2018 Copyright held by the owner/author(s). We introduce inference metaprogramming for probabilist...
We introduce and demonstrate a new ap-proach to inference in expressive probabilis-tic programming l...
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Comput...
Probabilistic symbolic execution aims at quantifying the probability of reaching program events of i...
Probabilistic models used in quantitative sciences have historically co-evolved with methods for per...
Probabilistic software analysis seeks to quantify the likelihood of reaching a target event under un...
One of the current challenges in artificial intelligence is modeling dynamic environments that chang...
Probabilistic modeling and reasoning are central tasks in artificial intelligence and machine learni...
Probabilistic logic programs are logic programs in which some of the facts are annotated with probab...
Researchers have recently proposed several systems that ease the process of developing Bayesian prob...
Probabilistic programming is becoming an attractive approach to probabilistic machine learning. Thro...
Synchronous reactive languages were introduced for designing and implementing real-time control soft...
In order to handle real-world problems, state-of-the-art probabilistic logic and learning frameworks...
Efficient inference is often possible in a streaming context using Rao-Blackwellized particle filter...
Probabilistic programming languages aid developers performing Bayesian in...
© 2018 Copyright held by the owner/author(s). We introduce inference metaprogramming for probabilist...
We introduce and demonstrate a new ap-proach to inference in expressive probabilis-tic programming l...
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Comput...
Probabilistic symbolic execution aims at quantifying the probability of reaching program events of i...
Probabilistic models used in quantitative sciences have historically co-evolved with methods for per...
Probabilistic software analysis seeks to quantify the likelihood of reaching a target event under un...
One of the current challenges in artificial intelligence is modeling dynamic environments that chang...
Probabilistic modeling and reasoning are central tasks in artificial intelligence and machine learni...
Probabilistic logic programs are logic programs in which some of the facts are annotated with probab...
Researchers have recently proposed several systems that ease the process of developing Bayesian prob...
Probabilistic programming is becoming an attractive approach to probabilistic machine learning. Thro...
Synchronous reactive languages were introduced for designing and implementing real-time control soft...
In order to handle real-world problems, state-of-the-art probabilistic logic and learning frameworks...