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

03/27/2013
by   Jonathan Stillman, et al.
0

We introduce a new heuristic algorithm for the problem of finding minimum size loop cutsets in multiply connected belief networks. We compare this algorithm to that proposed in [Suemmondt and Cooper, 1988]. We provide lower bounds on the performance of these algorithms with respect to one another and with respect to optimal. We demonstrate that no heuristic algorithm for this problem cam be guaranteed to produce loop cutsets within a constant difference from optimal. We discuss experimental results based on randomly generated networks, and discuss future work and open questions.

READ FULL TEXT

page 1

page 2

page 3

page 5

page 6

page 7

page 8

research
03/27/2013

Updating Probabilities in Multiply-Connected Belief Networks

This paper focuses on probability updates in multiply-connected belief n...
research
02/27/2013

Approximation Algorithms for the Loop Cutset Problem

We show how to find a small loop curser in a Bayesian network. Finding s...
research
08/07/2014

Random Algorithms for the Loop Cutset Problem

We show how to find a minimum loop cutset in a Bayesian network with hig...
research
06/01/2011

Randomized Algorithms for the Loop Cutset Problem

We show how to find a minimum weight loop cutset in a Bayesian network w...
research
02/27/2013

Properties of Bayesian Belief Network Learning Algorithms

Bayesian belief network learning algorithms have three basic components:...
research
09/03/2019

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 ...
research
04/28/2023

Finding agreement cherry-reduced subnetworks in level-1 networks

Phylogenetic networks are increasingly being considered as better suited...

Please sign up or login with your details

Forgot password? Click here to reset