40 citations · 74 across the 4 of their papers we have counts for
5 papers · 1 filter
Lifted Marginal MAP Inference
Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal +2
Lifted inference reduces the complexity of inference in relational probabilistic models by identifying groups of constants (or atoms) which behave symmetric to each other. A number…
Lifted Region-Based Belief Propagation
David Smith, Parag Singla, Vibhav Gogate
Due to the intractable nature of exact lifted inference, research has recently focused on the discovery of accurate and efficient approximate inference algorithms in Statistical Re…
Probabilistic Inference Modulo Theories
Rodrigo de Salvo Braz, Ciaran O'Reilly, Vibhav Gogate +1
We present SGDPLL(T), an algorithm that solves (among many other problems) probabilistic inference modulo theories, that is, inference problems over probabilistic models defined vi…
Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints
Vibhav Gogate, Rina Dechter
In this paper, we consider Hybrid Mixed Networks (HMN) which are Hybrid Bayesian Networks that allow discrete deterministic information to be modeled explicitly in the form of cons…
Modeling Transportation Routines using Hybrid Dynamic Mixed Networks
Vibhav Gogate, Rina Dechter, Bozhena Bidyuk +2
This paper describes a general framework called Hybrid Dynamic Mixed Networks (HDMNs) which are Hybrid Dynamic Bayesian Networks that allow representation of discrete deterministic…