Electronics Letters, 2008 The Laguerre method for the numerical inversion of Laplace transforms is a well known approach to the approximation of probability density functions (PDFs) and cumulative distribution functions (CDFs) of first passage times in Markov chains. Results are presented that relate the Laguerre generating functions and Laguerre coefficients of a PDF with ...
Harini Kulatunga, W.J. Knottenbelt
openalex +2 more sources
Data-driven methods to estimate the committor function in conceptual ocean models [PDF]
Nonlinear Processes in Geophysics, 2023 In recent years, several climate subsystems have been identified that may undergo a relatively rapid transition compared to the changes in their forcing.
V. Jacques-Dumas +5 more
doaj +1 more source
INFERRING OF REGULATORY NETWORKS FROM EXPRESSION DATA USING BAYESIAN NETWORKS [PDF]
Научно-технический вестник информационных технологий, механики и оптики, 2020 Subject of Research. The paper considers the inferring of gene regulatory networks in the form of Bayesian networks from gene expression data. We present this problem as the problem of the marginal probability estimation for each edge appearance in the ...
Alexander A. Loboda +1 more
doaj +1 more source
Computing performability measures in Markov chains by means of matrix functions [PDF]
Journal of Computational and Applied Mathematics, 2018 We discuss the efficient computation of performance, reliability, and availability measures for Markov chains; these metrics, and the ones obtained by combining them, are often called performability measures.
G. Masetti, L. Robol
semanticscholar +1 more source
Faster quantum mixing for slowly evolving sequences of Markov chains [PDF]
Quantum, 2018 Markov chain methods are remarkably successful in computational physics, machine learning, and combinatorial optimization. The cost of such methods often reduces to the mixing time, i.e., the time required to reach the steady state of the Markov chain ...
Davide Orsucci +2 more
doaj +1 more source
Robustness analysis of stochastic biochemical systems. [PDF]
PLoS ONE, 2014 We propose a new framework for rigorous robustness analysis of stochastic biochemical systems that is based on probabilistic model checking techniques. We adapt the general definition of robustness introduced by Kitano to the class of stochastic systems ...
Milan Ceska +3 more
doaj +1 more source
Inexact Uniformization Method for Computing Transient Distributions of Markov Chains [PDF]
SIAM Journal on Scientific Computing, 2007 The uniformization method (also known as randomization) is a numerically stable algorithm for computing transient distributions of a continuous time Markov chain. When the solution is needed after a long run or when the convergence is slow, the uniformization method involves a large number of matrix-vector products.
Roger B. Sidje +2 more
openaire +3 more sources
Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons. [PDF]
PLoS Computational Biology, 2011 The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of ...
Lars Buesing +3 more
doaj +1 more source
Comparison of asymptotic variances of inhomogeneous Markov chains with application to Markov chain Monte Carlo methods [PDF]
, 2013 In this paper, we study the asymptotic variance of sample path averages for inhomogeneous Markov chains that evolve alternatingly according to two different $\pi$-reversible Markov transition kernels $P$ and $Q$. More specifically, our main result allows
Florian Maire, R. Douc, J. Olsson
semanticscholar +1 more source
Simple, fast and accurate implementation of the diffusion approximation algorithm for stochastic ion channels with multiple states. [PDF]
PLoS ONE, 2012 BACKGROUND: The phenomena that emerge from the interaction of the stochastic opening and closing of ion channels (channel noise) with the non-linear neural dynamics are essential to our understanding of the operation of the nervous system.
Patricio Orio, Daniel Soudry
doaj +1 more source