We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Parallell interacting MCMC for learning of topologies of graphical models.
- Authors
Corander, Jukka; Ekdahl, Magnus; Koski, Timo
- Abstract
Automated statistical learning of graphical models from data has attained a considerable degree of interest in the machine learning and related literature. Many authors have discussed and/or demonstrated the need for consistent stochastic search methods that would not be as prone to yield locally optimal model structures as simple greedy methods. However, at the same time most of the stochastic search methods are based on a standard Metropolis–Hastings theory that necessitates the use of relatively simple random proposals and prevents the utilization of intelligent and efficient search operators. Here we derive an algorithm for learning topologies of graphical models from samples of a finite set of discrete variables by utilizing and further enhancing a recently introduced theory for non-reversible parallel interacting Markov chain Monte Carlo-style computation. In particular, we illustrate how the non-reversible approach allows for novel type of creativity in the design of search operators. Also, the parallel aspect of our method illustrates well the advantages of the adaptive nature of search operators to avoid trapping states in the vicinity of locally optimal network topologies.
- Subjects
MARKOV processes; MONTE Carlo method; GRAPHICAL modeling (Statistics); MACHINE learning; STOCHASTIC analysis; EQUIVALENCE relations (Set theory); TOPOLOGY; GRAPHIC methods for multivariate analysis; ALGORITHMS
- Publication
Data Mining & Knowledge Discovery, 2008, Vol 17, Issue 3, p431
- ISSN
1384-5810
- Publication type
Article
- DOI
10.1007/s10618-008-0099-9