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63
Pagerank without hyperlinks: structural reranking using links induced by language models
 In Proceedings of SIGIR
, 2005
"... Inspired by the PageRank and HITS (hubs and authorities) algorithms for Web search, we propose a structural reranking approach to ad hoc information retrieval: we reorder the documents in an initially retrieved set by exploiting asymmetric relationships between them. Specifically, we consider gener ..."
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Cited by 103 (14 self)
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Inspired by the PageRank and HITS (hubs and authorities) algorithms for Web search, we propose a structural reranking approach to ad hoc information retrieval: we reorder the documents in an initially retrieved set by exploiting asymmetric relationships between them. Specifically, we consider generation links, which indicate that the language model induced from one document assigns high probability to the text of another; in doing so, we take care to prevent bias against long documents. We study a number of reranking criteria based on measures of centrality in the graphs formed by generation links, and show that integrating centrality into standard languagemodelbased retrieval is quite effective at improving precision at top ranks.
Stochastic complementation, uncoupling Markov chains, and the theory of nearly reducible systems
 SIAM Rev
, 1989
"... Abstract. A concept called stochastic complementation is an idea which occurs naturally, although not always explicitly, in the theory and application of finite Markov chains. This paper brings this idea to the forefront with an explicit definition and a development of some of its properties. Applic ..."
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Cited by 97 (7 self)
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Abstract. A concept called stochastic complementation is an idea which occurs naturally, although not always explicitly, in the theory and application of finite Markov chains. This paper brings this idea to the forefront with an explicit definition and a development of some of its properties. Applications of stochastic complementation are explored with respect to problems involving uncoupling procedures in the theory of Markov chains. Furthermore, the role of stochastic complementation in the development of the classical Simon–Ando theory of nearly reducible system is presented. Key words. Markov chains, stationary distributions, stochastic matrix, stochastic complementation, nearly reducible systems, Simon–Ando theory
A New Approach to Model the Stationary Behavior of TCP Connections
 in Proceedings of IEEE INFOCOM 2000
, 2000
"... In this paper, we outline a methodology that can be applied to model the behavior of TCP flows. The proposed methodology stems from a Markovian model of a single TCP source, and eventually considers the superposition and interaction of several such sources using standard queueing analysis techniques ..."
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Cited by 58 (5 self)
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In this paper, we outline a methodology that can be applied to model the behavior of TCP flows. The proposed methodology stems from a Markovian model of a single TCP source, and eventually considers the superposition and interaction of several such sources using standard queueing analysis techniques. Our approach allows the evaluation of such performance indices as throughput, queueing delay and packet loss of TCP flows. The results obtained through our model are validated by means of simulation, under several topology and traffic settings. I. INTRODUCTION According to recent estimates 95% of the traffic carried today over widearea IP networks uses TCP as transport protocol, which amounts to 80% of the overall endtoend flow count. These figures alone highlight the key role that TCP plays in delivering a reliable service to the most common network applications such as email programs and Web browsers. During the many years of "honorable" service, TCP has undergone several alteration...
Folding algorithm: A computational method for finite qbd processes with leveldependent transitions
 IEEE Transactions on Communications
, 1994
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Numerical Methods in Markov Chain Modelling
 Operations Research
, 1996
"... This paper describes and compares several methods for computing stationary probability distributions of Markov chains. The main linear algebra problem consists of computing an eigenvector of a sparse, nonsymmetric, matrix associated with a known eigenvalue. It can also be cast as a problem of solvi ..."
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Cited by 36 (8 self)
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This paper describes and compares several methods for computing stationary probability distributions of Markov chains. The main linear algebra problem consists of computing an eigenvector of a sparse, nonsymmetric, matrix associated with a known eigenvalue. It can also be cast as a problem of solving a homogeneous, singular linear system. We present several methods based on combinations of Krylov subspace techniques, single vector power iteration/relaxation procedures and acceleration techniques. We compare the performance of these methods on some realistic problems. Key words: Markov chain models; Homogeneous linear systems; Direct methods; Successive Overrelaxation; Preconditioned power iterations; Arnoldi's method; GMRES. y IRISA, Rennes, France. Research supported by CNRS (87:N 920070). Research Institute for Advanced Computer Science, NASA Ames Research Center. Moffett Field CA 94035. Research supported by Cooperative Agreement NCC 2387 between the National Aeronautics and S...
Sensitivity Of The Stationary Distribution Of A Markov Chain
 SIAM Journal on Matrix Analysis and Applications
, 1994
"... . It is well known that if the transition matrix of an irreducible Markov chain of moderate size has a subdominant eigenvalue which is close to 1, then the chain is ill conditioned in the sense that there are stationary probabilities which are sensitive to perturbations in the transition probabiliti ..."
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Cited by 28 (2 self)
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. It is well known that if the transition matrix of an irreducible Markov chain of moderate size has a subdominant eigenvalue which is close to 1, then the chain is ill conditioned in the sense that there are stationary probabilities which are sensitive to perturbations in the transition probabilities. However, the converse of this statement has heretofore been unresolved. The purpose of this article is to address this issue by establishing upper and lower bounds on the condition number of the chain such that the bounding terms are functions of the eigenvalues of the transition matrix. Furthermore, it is demonstrated how to obtain estimates for the condition number of an irreducible chain with little or no extra computational e#ort over that required to compute the stationary probabilities by means of an LU or QR factorization. Key words. Markov chains, stationary distribution, stochastic matrix, sensitivity analysis, perturbation theory, character of a Markov chain, condition numbers ...
Uniform Stability Of Markov Chains
 SIAM J. MATRIX ANAL. APPL
, 1994
"... By deriving a new set of tight perturbation bounds, it is shown that all stationary probabilities of a finite irreducible Markov chain react essentially in the same way to perturbations in the transition probabilities. In particular, if at least one stationary probability is insensitive in a relativ ..."
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Cited by 27 (7 self)
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By deriving a new set of tight perturbation bounds, it is shown that all stationary probabilities of a finite irreducible Markov chain react essentially in the same way to perturbations in the transition probabilities. In particular, if at least one stationary probability is insensitive in a relative sense, then all stationary probabilities must be insensitive in an absolute sense. New measures of sensitivity are related to more traditional ones, and it is shown that all relevant condition numbers for the Markov chain problem are small multiples of each other. Finally, the implications of these findings to the computation of stationary probabilities by direct methods are discussed, and the results are applied to stability issues in nearly transient chains.
Structured Analysis Approaches for Large Markov Chains  A Tutorial
 Applied Numerical Mathematics
, 1996
"... The tutorial introduces structured analysis approaches for continuous time Markov chains (CTMCs) which are a means to extend the size of analyzable state spaces significantly compared with conventional techniques. It is shown how generator matrices of large CTMCs can be represented in a very compact ..."
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Cited by 22 (9 self)
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The tutorial introduces structured analysis approaches for continuous time Markov chains (CTMCs) which are a means to extend the size of analyzable state spaces significantly compared with conventional techniques. It is shown how generator matrices of large CTMCs can be represented in a very compact form, how this representation can be exploited in numerical solution techniques and how numerical analysis profits from this exploitation. Additionally, recent results covering implementation issues, tool support, and advanced analysis techniques are surveyed. 1 Introduction Analysis of continuous time Markov chains (CTMCs) is a well established approach to analyze the performance, dependability and performability of computer and communication systems. Systems are modeled using specification techniques like queueing networks (QNs), stochastic Petri nets (SPNs), formal specification techniques to mention only a few. Unfortunately, the size of CTMCs underlying most realistic examples can be ...
Numerical Methods for Computing Stationary Distributions of Finite Irreducible Markov Chains
 of Advances in Computational Probability
, 1997
"... Introduction In this chapter our attention will be devoted to computational methods for computing stationary distributions of finite irreducible Markov chains. We let q ij denote the rate at which an nstate Markov chain moves from state i to state j. The n \Theta n matrix Q whose offdiagonal ele ..."
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Cited by 20 (0 self)
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Introduction In this chapter our attention will be devoted to computational methods for computing stationary distributions of finite irreducible Markov chains. We let q ij denote the rate at which an nstate Markov chain moves from state i to state j. The n \Theta n matrix Q whose offdiagonal elements are q ij and whose i th diagonal element is given by \Gamma P n j=1;j 6=i q ij is called the infinitesimal generator of the Markov chain. It may be shown that the stationary probability vector ß, a row vector whose kth element denotes the stationary probability of being in state k, can be obtained by solving the homogeneous system of equations ßQ<F34
Structured Markov chains solver: software tools
 Proceedings of SMCTOOLS, Pisa 2006
, 2006
"... The package SMCSolver for solving structured Markov chains is presented. It contains the most advanced algorithms for solving QBD, M/G/1 and G/M/1 problems. The package is provided in two versions: a Matlab toolbox and a Fortran 95 version with a userfriendly graphical interface. 1. ..."
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Cited by 19 (7 self)
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The package SMCSolver for solving structured Markov chains is presented. It contains the most advanced algorithms for solving QBD, M/G/1 and G/M/1 problems. The package is provided in two versions: a Matlab toolbox and a Fortran 95 version with a userfriendly graphical interface. 1.