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139
NonUniform Random Variate Generation
, 1986
"... This is a survey of the main methods in nonuniform random variate generation, and highlights recent research on the subject. Classical paradigms such as inversion, rejection, guide tables, and transformations are reviewed. We provide information on the expected time complexity of various algorith ..."
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Cited by 1009 (25 self)
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This is a survey of the main methods in nonuniform random variate generation, and highlights recent research on the subject. Classical paradigms such as inversion, rejection, guide tables, and transformations are reviewed. We provide information on the expected time complexity of various algorithms, before addressing modern topics such as indirectly specified distributions, random processes, and Markov chain methods.
Error bounds for computing the expectation by Markov chain Monte Carlo
, 2009
"... We study the error of reversible Markov chain Monte Carlo methods for approximating the expectation of a function. Explicit error bounds with respect to the l2, l4 and l∞norm of the function are proven. By the estimation the well known asymptotical limit of the error is attained, i.e. there is n ..."
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Cited by 115 (2 self)
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We study the error of reversible Markov chain Monte Carlo methods for approximating the expectation of a function. Explicit error bounds with respect to the l2, l4 and l∞norm of the function are proven. By the estimation the well known asymptotical limit of the error is attained, i.e. there is no gap between the estimate and the asymptotical behavior. We discuss the dependence of the error on a burnin of the Markov chain. Furthermore we suggest and justify a specific burnin for optimizing the algorithm.
Structured learning and prediction in computer vision
 IN FOUNDATIONS AND TRENDS IN COMPUTER GRAPHICS AND VISION
, 2010
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Multiple Indicators, partially ordered sets, and linear extensions: Multicriterion ranking and prioritization
, 2004
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MASTR: multiple alignment and structure prediction of noncoding RNAs using simulated annealing
 Bioinformatics
, 2007
"... Motivation: As more non–coding RNAs are discovered, the importance of methods for RNA analysis increases. Since the structure of ncRNA is intimately tied to the function of the molecule, programs for RNA structure prediction are necessary tools in this growing field of research. Furthermore, it is ..."
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Cited by 28 (2 self)
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Motivation: As more non–coding RNAs are discovered, the importance of methods for RNA analysis increases. Since the structure of ncRNA is intimately tied to the function of the molecule, programs for RNA structure prediction are necessary tools in this growing field of research. Furthermore, it is known that RNA structure is often evolutionarily more conserved than sequence. However, few existing methods are capable of simultaneously considering multiple sequence alignment and structure prediction. Results: We present a novel solution to the problem of simultaneous structure prediction and multiple alignment of RNA sequences. Using Markov chain Monte Carlo in a simulated annealing framework, the algorithm MASTR (Multiple Alignment of ST ructural RNAs) iteratively improves both sequence alignment and structure prediction for a set of RNA sequences. This is done by minimizing a combined cost function that considers sequence conservation, covariation and basepairing probabilities. The results show that the method is very competitive to similar programs available today, both in terms of accuracy and computational efficiency.
Distributed Routing in SmallWorld Networks
, 2007
"... So called smallworld networks – clustered networks with small diameters – are thought to be prevalent in nature, especially appearing in people’s social interactions. Many models exist for this phenomenon, with some of the most recent explaining how it is possible to find short routes between nodes ..."
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Cited by 27 (3 self)
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So called smallworld networks – clustered networks with small diameters – are thought to be prevalent in nature, especially appearing in people’s social interactions. Many models exist for this phenomenon, with some of the most recent explaining how it is possible to find short routes between nodes in such networks. Searching for such routes, however, always depends on nodes knowing what their and their neighbors positions are relative to the destination. In real applications where one may wish to search a smallworld network, such as peertopeer computer networks, this cannot always be assumed to be true. We propose and explore a method of routing that does not depend on such knowledge, and which can be implemented in a completely distributed way without any global elements. The Markov Chain MonteCarlo based algorithm takes only a graph as input, and requires no further information about the nodes themselves. The proposed method is tested against simulated and real world data.
On the exact simulation of functionals of stationary markov chains. Linear Algebra and its Applications 386:285–310
, 2004
"... In performance evaluation domain, simulation is an alternative when numerical analysis fail. To avoid the burnin time problem, this paper presents an adaptation of the perfect simulation algorithm [10] to finite ergodic Markov chain with arbitrary structure. Simulation algorithms are deduced and p ..."
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Cited by 20 (8 self)
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In performance evaluation domain, simulation is an alternative when numerical analysis fail. To avoid the burnin time problem, this paper presents an adaptation of the perfect simulation algorithm [10] to finite ergodic Markov chain with arbitrary structure. Simulation algorithms are deduced and provide samplings of functionals of the steadystate without computing the state coupling, it speeds up the algorithm by a significant factor. Based on a sparse representation of the Markov chain, the aliasing technique improves highly the complexity of the simulation. Moreover, with small adaptations, it builds a transition function algorithm that ensures coupling. Key words: Markov chain simulation, perfect simulation, steadystate analysis. 1
Bounds for the coupling time in queueing networks perfect simulation
 In Numerical Solutions for Markov Chain (NSMC06
, 2006
"... a p por t d e r e c h e r c h e ..."
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