135 citations · 307 across the 31 of their papers we have counts for
7 papers · 2 filters
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…
Studies in Lower Bounding Probabilities of Evidence using the Markov Inequality
Vibhav Gogate, Bozhena Bidyuk, Rina Dechter
Computing the probability of evidence even with known error bounds is NP-hard. In this paper we address this hard problem by settling on an easier problem. We propose an approximat…
AND/OR Importance Sampling
Vibhav Gogate, Rina Dechter
The paper introduces AND/OR importance sampling for probabilistic graphical models. In contrast to importance sampling, AND/OR importance sampling caches samples in the AND/OR spac…
Formula-Based Probabilistic Inference
Vibhav Gogate, Pedro Domingos
Computing the probability of a formula given the probabilities or weights associated with other formulas is a natural extension of logical inference to the probabilistic setting. S…
Probabilistic Theorem Proving
Vibhav Gogate, Pedro Domingos
Many representation schemes combining first-order logic and probability have been proposed in recent years. Progress in unifying logical and probabilistic inference has been slower…
Approximation by Quantization
Vibhav Gogate, Pedro Domingos
Inference in graphical models consists of repeatedly multiplying and summing out potentials. It is generally intractable because the derived potentials obtained in this way can be…