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Decision diagrams for the approximate analysis of Markov models
Author(s) -
Ciardo Gianfranco
Publication year - 2007
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.200700833
Subject(s) - markov chain , markov decision process , markov model , computation , computer science , encode , state space , stochastic matrix , state (computer science) , algorithm , variable order markov model , markov process , markov kernel , matrix (chemical analysis) , decision model , theoretical computer science , mathematics , machine learning , statistics , biochemistry , chemistry , gene , materials science , composite material
Decision diagrams of various types can be used to encode the exact state space and transition rate matrix of large Markov models. However, the exact solution of such models still requires to store at least one real vector with one entry per reachable state, a formidable limitation to the practical use of these encodings. Thus, we discuss automatic techniques for the approximate computation of performance measures when the Markov model can be compactly encoded but not exactly solved. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)