A General MCMC Method for Bayesian Inference in Logic-Based Probabilistic Modeling
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1472-1476.
Abstract
We propose a general MCMC method for Bayesian inference in logic-based probabilistic modeling. It covers a broad class of generative models including Bayesian networks and PCFGs. The idea is to generalize an MCMC method for PCFGs to the one for a Turing-complete probabilistic modeling language PRISM in the context of statistical abduction where parse trees are replaced with explanations. We describe how to estimate the marginal probability of data from MCMC samples and how to perform Bayesian Viterbi inference using an example of Naive Bayes model augmented with a hidden variable.
@inproceedings{ijcai2011p248,
title = {A General MCMC Method for Bayesian Inference in Logic-Based Probabilistic Modeling},
author = {Sato, Taisuke},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {1472--1476},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-248},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-248},
}
TY - CPAPER TI - A General MCMC Method for Bayesian Inference in Logic-Based Probabilistic Modeling AU - Sato, Taisuke T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 1472 EP - 1476 DO - 10.5591/978-1-57735-516-8/IJCAI11-248 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-248 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: