Polynomial-time derivation of optimal k-tree topology from Markov networks

arXiv:2404.05991v1 Announce Type: cross
Abstract: Characterization of joint probability distribution for large networks of random variables remains a challenging task in data science. Probabilistic graph approximation with simple topologies has practically been resorted to; typically the tree topology makes joint probability computation much simpler and can be effective for statistical inference on insufficient data. However, to characterize network components where multiple variables cooperate closely to influence others, model topologies beyond a tree are needed, which unfortunately are infeasible to acquire. …

abstract approximation arxiv computation cs.ds data data science derivation distribution graph inference markov networks polynomial probability random science simple statistical stat.ml topology tree type variables