
Randomized Algorithms for the Loop Cutset Problem
We show how to find a minimum weight loop cutset in a Bayesian network w...
read it

Approximation Algorithms for the Loop Cutset Problem
We show how to find a small loop curser in a Bayesian network. Finding s...
read it

Closedloop field development optimization with multipoint geostatistics and statistical assessment
Closedloop field development (CLFD) optimization is a comprehensive fra...
read it

Loop Summarization with Rational Vector Addition Systems (extended version)
This paper presents a technique for computing numerical loop summaries. ...
read it

Loop Programming Practices that Simplify Quicksort Implementations
Quicksort algorithm with Hoare's partition scheme is traditionally imple...
read it

On Heuristics for Finding Loop Cutsets in MultiplyConnected Belief Networks
We introduce a new heuristic algorithm for the problem of finding minimu...
read it

De(con)struction of the lazyF loop: improving performance of Smith Waterman alignment
Striped variation of the SmithWaterman algorithm is known as extremely ...
read it
Random Algorithms for the Loop Cutset Problem
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.
READ FULL TEXT
Comments
There are no comments yet.