
Randomized heuristic for the maximum clique problem
A clique in a graph is a set of vertices that are all directly connected...
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Logarithmic Time Parallel Bayesian Inference
I present a parallel algorithm for exact probabilistic inference in Baye...
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Region Based Approximation for High Dimensional Bayesian Network Models
Performing efficient inference on Bayesian Networks (BNs), with large nu...
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Is the space complexity of planted clique recovery the same as that of detection?
We study the planted clique problem in which a clique of size k is plant...
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Symbolic Probabilistic Inference with Evidence Potential
Recent research on the Symbolic Probabilistic Inference (SPI) algorithm[...
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Sidestepping the Triangulation Problem in Bayesian Net Computations
This paper presents a new approach for computing posterior probabilities...
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Large deviations for the largest eigenvalue of Gaussian networks with constant average degree
Large deviation behavior of the largest eigenvalue λ_1 of Gaussian netwo...
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A Sufficiently Fast Algorithm for Finding Close to Optimal Junction Trees
An algorithm is developed for finding a close to optimal junction tree of a given graph G. The algorithm has a worst case complexity O(c^k n^a) where a and c are constants, n is the number of vertices, and k is the size of the largest clique in a junction tree of G in which this size is minimized. The algorithm guarantees that the logarithm of the size of the state space of the heaviest clique in the junction tree produced is less than a constant factor off the optimal value. When k = O(log n), our algorithm yields a polynomial inference algorithm for Bayesian networks.
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