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An information theory based optimality criterion to sample stationary distributions of Markov chains

Giovanni Trezza

An information theory based optimality criterion to sample stationary distributions of Markov chains.

Rel. Eliodoro Chiavazzo, Gerhard Hummer, Roberto Covino, Matteo Fasano. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Meccanica, 2019

Abstract:

Biomechanical and molecular systems are complex, high-dimensional systems whose dynamics is described by stochastic laws. Numerical simulations are an invaluable tool to investigate the dynamics and equilibrium properties of these systems. However, the complexity of the calculation often limits severely the sampling, such that many interesting time scales are out of reach. In this thesis, I investigate an information theory inspired algorithm to efficiently invest computational resources to speed-up the sampling of the system’s equilibrium distribution. I introduce a sampling scheme that opportunely reinitialize simulations to obtain a faster convergence of the stationary distributions compared to a brute-force sampling. The methodological framework is based on probability and information theory applied to stochastic Markovian processes. The theory and the results reported in this work are possibly relevant to biological systems and more in general to any stochastic Markovian processes.

Relators: Eliodoro Chiavazzo, Gerhard Hummer, Roberto Covino, Matteo Fasano
Academic year: 2019/20
Publication type: Electronic
Number of Pages: 64
Additional Information: Tesi secretata. Fulltext non presente
Subjects:
Corso di laurea: Corso di laurea magistrale in Ingegneria Meccanica
Classe di laurea: New organization > Master science > LM-33 - MECHANICAL ENGINEERING
Ente in cotutela: Max-Planck-Institut für Biophysik (GERMANIA)
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/13380
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