Timezone: »
Statistical relational learning models combine the power of first-order logic, the de facto tool for handling relational structure, with that of probabilistic graphical models, the de facto tool for handling uncertainty. Lifted probabilistic inference algorithms for them have been the subject of much recent research. The main idea in these algorithms is to improve the speed, accuracy and scalability of existing graphical models' inference algorithms by exploiting symmetry in the first-order representation. In this paper, we consider blocked Gibbs sampling, an advanced variation of the classic Gibbs sampling algorithm and lift it to the first-order level. We propose to achieve this by partitioning the first-order atoms in the relational model into a set of disjoint clusters such that exact lifted inference is polynomial in each cluster given an assignment to all other atoms not in the cluster. We propose an approach for constructing such clusters and determining their complexity and show how it can be used to trade accuracy with computational complexity in a principled manner. Our experimental evaluation shows that lifted Gibbs sampling is superior to the propositional algorithm in terms of accuracy and convergence.
Author Information
Deepak Venugopal (The University of Texas at Dallas (UT Dallas))
Vibhav G Gogate (UT Dallas)
More from the Same Authors
-
2015 Poster: Bounding the Cost of Search-Based Lifted Inference »
David B Smith · Vibhav G Gogate -
2015 Poster: Fast Lifted MAP Inference via Partitioning »
Somdeb Sarkhel · Parag Singla · Vibhav G Gogate -
2015 Poster: Lifted Inference Rules With Constraints »
Happy Mittal · Anuj Mahajan · Vibhav G Gogate · Parag Singla -
2014 Poster: An Integer Polynomial Programming Based Framework for Lifted MAP Inference »
Somdeb Sarkhel · Deepak Venugopal · Parag Singla · Vibhav G Gogate -
2014 Poster: New Rules for Domain Independent Lifted MAP Inference »
Happy Mittal · Prasoon Goyal · Vibhav G Gogate · Parag Singla -
2014 Poster: Scaling-up Importance Sampling for Markov Logic Networks »
Deepak Venugopal · Vibhav G Gogate -
2010 Poster: Learning Efficient Markov Networks »
Vibhav G Gogate · William A Webb · Pedro Domingos -
2010 Poster: Lifted Inference Seen from the Other Side : The Tractable Features »
Abhay Jha · Vibhav G Gogate · Alexandra Meliou · Dan Suciu
