---
title: "A General MCMC Method for Bayesian Inference in Logic-Based Probabilistic Modeling"
authors:
  - "Taisuke Sato"
doi: "10.5591/978-1-57735-516-8/IJCAI11-248"
url: "https://ijcai.org/proceedings/2011/248"
pdf: "https://ijcai.org/proceedings/2011/0248.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "1472-1476"
track: ""
keywords:
---

# A General MCMC Method for Bayesian Inference in Logic-Based Probabilistic Modeling

Taisuke Sato

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 1472-1476. DOI: [10.5591/978-1-57735-516-8/IJCAI11-248](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-248)

## 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.