Title
Probabilistic Abductive Logic Programming using Dirichlet Priors.
Abstract
Probabilistic programming is an area of research that aims to develop general inference algorithms for probabilistic models expressed as probabilistic programs whose execution corresponds to inferring the parameters of those models. In this paper, we introduce a probabilistic programming language (PPL) based on abductive logic programming for performing inference in probabilistic models involving categorical distributions with Dirichlet priors. We encode these models as abductive logic programs enriched with probabilistic definitions and queries, and show how to execute and compile them to boolean formulas. Using the latter, we perform generalized inference using one of two proposed Markov Chain Monte Carlo (MCMC) sampling algorithms: an adaptation of uncollapsed Gibbs sampling from related work and a novel collapsed Gibbs sampling (CGS). We show that CGS converges faster than the uncollapsed version on a latent Dirichlet allocation (LDA) task using synthetic data. On similar data, we compare our PPL with LDA-specific algorithms and other PPLs. We find that all methods, except one, perform similarly and that the more expressive the PPL, the slower it is. We illustrate applications of our PPL on real data in two variants of LDA models (Seed and Cluster LDA), and in the repeated insertion model (RIM). In the latter, our PPL yields similar conclusions to inference with EM for Mallows models. A probabilistic programming language for categorical models with Dirichlet priors.A representation of categorical variables: conditional annotated disjunction compilation.A collapsed Gibbs sampling algorithm for categorical models with Dirichlet priors.We show that collapsed Gibbs sampling converges faster than uncollapsed on LDA.We show inference results on real data using LDA and the repeated insertion model.
Year
DOI
Venue
2016
10.1016/j.ijar.2016.07.001
Int. J. Approx. Reasoning
Keywords
DocType
Volume
Probabilistic programming,Abductive logic programming,Markov Chain Monte Carlo,Latent Dirichlet allocation,Repeated insertion model
Journal
78
Issue
ISSN
Citations 
1
0888-613X
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Calin-Rares Turliuc111.02
Luke Dickens2776.18
Alessandra Russo3102280.10
krysia broda425532.16