23 citations · 61 across the 7 of their papers we have counts for
6 papers · 1 filter
Active Tuples-based Scheme for Bounding Posterior Beliefs
Bozhena Bidyuk, Rina Dechter, Emma Rollon
The paper presents a scheme for computing lower and upper bounds on the posterior marginals in Bayesian networks with discrete variables. Its power lies in its ability to use any a…
An Empirical Study of w-Cutset Sampling for Bayesian Networks
Bozhena Bidyuk, Rina Dechter
The paper studies empirically the time-space trade-off between sampling and inference in a sl cutset sampling algorithm. The algorithm samples over a subset of nodes in a Bayesian…
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…
Cutset Sampling with Likelihood Weighting
Bozhena Bidyuk, Rina Dechter
The paper analyzes theoretically and empirically the performance of likelihood weighting (LW) on a subset of nodes in Bayesian networks. The proposed scheme requires fewer samples…
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…
Cutset Sampling for Bayesian Networks
B. Bidyuk, R. Dechter
The paper presents a new sampling methodology for Bayesian networks that samples only a subset of variables and applies exact inference to the rest. Cutset sampling is a network st…