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We apply the Metropolis-Hastings algorithm to efficiently sample from arbitrary paths distributions in a general network. Paths can be generalized into all-day travel plans through, e.g., an appropriate network expansion. The Metropolis-Hastings algorithm ...
In this thesis we investigate different ways of approximating the solution of the chemical master equation (CME). The CME is a system of differential equations that models the stochastic transient behaviour of biochemical reaction networks. It does so by d ...
We present a simple, effective generalisation of variable order Markov models to full online Bayesian estimation. The mechanism used is close to that employed in context tree weighting. The main contribution is the addition of a prior, conditioned on conte ...
In this paper, we propose a Markov chain modeling of complicated phenomena observed from coupled chaotic oscillators. Once we obtain the transition probability matrix from computer simulation results, various statistical quantities can be easily calculated ...
Markov chain Monte Carlo has been the standard technique for inferring the posterior distribution of genome rearrangement scenarios under a Bayesian approach. We present here a negative result on the rate of convergence of the generally used Markov chains. ...
This paper investigates the limit behavior of Markov decision processes made of independent particles evolving in a common environment, when the number of particles goes to infinity. In the finite horizon case or with a discounted cost and an infinite hori ...
In this work, we present a novel way of computing the continuous Haar, Fourier and cosine series coefficients of rectilinear polygons. We derive algorithms to compute the inner products with the continuous basis functions directly from the vertices of the ...
This article presents the implementation and initial test results for an algorithm called SuffStat MCMC, which aims to speed up Approximate Bayesian Computation without likelihood. ...
Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad-hoc and ma ...
In this paper, we introduce a simple Monte Carlo method for simulating the dynamics of a crowd. Within our model a collection of hard-disk agents is subjected to a series of two-stage steps, implying (i) the displacement of one specific agent followed by ( ...
