It is well known that among all probabilistic graphical Markov models the class of decomposable models is the most advantageous in the sense that the respective distributions can b...
Partially observable Markov decision process (POMDP) is commonly used to model a stochastic environment with unobservable states for supporting optimal decision making. Computing ...
Abstract. Parameter estimation of large bio-pathway models is an important and difficult problem. To reduce the prohibitive computational cost, one approach is to decompose a large...
Geoffrey Koh, Lisa Tucker-Kellogg, David Hsu, P. S...
We present a new framework based on walks in a graph for analysis and inference in Gaussian graphical models. The key idea is to decompose the correlation between each pair of var...
Dmitry M. Malioutov, Jason K. Johnson, Alan S. Wil...
Low-dimensional topic models have been proven very useful for modeling a large corpus of documents that share a relatively small number of topics. Dimensionality reduction tools s...