Learning Optimal Chain Graphs with Answer Set Programming
Dag Sonntag, José M. Peña, Antti Hyttinen · 2016
Learning an optimal chain graph from data is an important hard computational problem. We present a new approach to solve this problem for various objective functions without making any assumption on the probability distribution at hand. Our approach is based on encoding the learning problem declaratively using the answer set programming (ASP) paradigm. Empirical re-sults show that our approach provides at least as accurate solutions as the best solutions provided by the existing algorithms, and overall provides better accuracy than any single previous algo-rithm. 1