@book{
 title = {Parameter identification for Markov models of biochemical reactions},
 type = {book},
 year = {2011},
 source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 volume = {6806 LNCS},
 id = {9abc68a3-b901-391a-8e8f-8fa8b3cb7073},
 created = {2017-12-21T13:50:26.265Z},
 file_attached = {false},
 profile_id = {bbb99b2d-2278-3254-820f-2de6d915ce63},
 last_modified = {2017-12-21T13:50:26.265Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {We propose a numerical technique for parameter inference in Markov models of biological processes. Based on time-series data of a process we estimate the kinetic rate constants by maximizing the likelihood of the data. The computation of the likelihood relies on a dynamic abstraction of the discrete state space of the Markov model which successfully mitigates the problem of state space largeness. We compare two variants of our method to state-of-the-art, recently published methods and demonstrate their usefulness and efficiency on several case studies from systems biology. © 2011 Springer-Verlag.},
 bibtype = {book},
 author = {Andreychenko, A. and Mikeev, L. and Spieler, D. and Wolf, V.},
 doi = {10.1007/978-3-642-22110-1_8}
}

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