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Linar Mikeev

dblp:27/8147 · DBLP profile ↗
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4ranked-venue papers
2as first author
0since 2021 · last 2013
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 4 · 2 first-authorSoftware engineering, systems software and programming languages · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Mathematical optimization · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Mathematical optimization › control theory › system identification
parameter identification		 0.112011
Parameter Identification for Markov Models of Biochemical Reactions · CAV 2011
Bioinformatics and computational biology › systems biology
biochemical reaction network		 0.012011
Parameter Identification for Markov Models of Biochemical Reactions · CAV 2011
YearPublicationVenuePosition
2013 On-the-fly verification and optimization of DTA-properties for large Markov chains
Linar Mikeev, Martin R. Neuhäußer, David Spieler, Verena Wolf 0001
 Formal Methods Syst. Des.1
2012 Parameter estimation for stochastic hybrid models of biochemical reaction networks
abstract
The dynamics of biochemical reaction networks can be accurately described by stochastic hybrid models, where we assume that large chemical populations evolve deterministically and continuously over time while small populations change through random discrete reactions.
Linar Mikeev, Verena Wolf 0001
 HSCC1
2011 Parameter Identification for Markov Models of Biochemical Reactions
Aleksandr Andreychenko, Linar Mikeev, David Spieler, Verena Wolf 0001
 CAV2
2011 SHAVE: stochastic hybrid analysis of markov population models
abstract
We present a tool called SHAVE that approximates the transient distribution of a continuous-time Markov population process by combining moment-based and state-based representations of probability distributions. As an intermediate step, SHAVE constructs a stochastic hybrid model from the original process which is then solved numerically.
Maksim Lapin, Linar Mikeev, Verena Wolf 0001
 HSCC2