Random Algorithms for the Loop Cutset Problem

by   Ann Becker, et al.

We show how to find a minimum loop cutset in a Bayesian network with high probability. Finding such a loop cutset is the first step in Pearl's method of conditioning for inference. Our random algorithm for finding a loop cutset, called "Repeated WGuessI", outputs a minimum loop cutset, after O(c 6^k k n) steps, with probability at least 1-(1 over6^k)^c 6^k), where c>1 is a constant specified by the user, k is the size of a minimum weight loop cutset, and n is the number of vertices. We also show empirically that a variant of this algorithm, called WRA, often finds a loop cutset that is closer to the minimum loop cutset than the ones found by the best deterministic algorithms known.


page 1

page 2

page 3

page 4

page 5

page 6

page 7

page 8


Randomized Algorithms for the Loop Cutset Problem

We show how to find a minimum weight loop cutset in a Bayesian network w...

Approximation Algorithms for the Loop Cutset Problem

We show how to find a small loop curser in a Bayesian network. Finding s...

Loop Summarization with Rational Vector Addition Systems (extended version)

This paper presents a technique for computing numerical loop summaries. ...

Loop Programming Practices that Simplify Quicksort Implementations

Quicksort algorithm with Hoare's partition scheme is traditionally imple...

Response surface single loop reliability-based design optimization with higher-order reliability assessment

Reliability-based design optimization (RBDO) aims at determination of th...

On Heuristics for Finding Loop Cutsets in Multiply-Connected Belief Networks

We introduce a new heuristic algorithm for the problem of finding minimu...

De(con)struction of the lazy-F loop: improving performance of Smith Waterman alignment

Striped variation of the Smith-Waterman algorithm is known as extremely ...