The 2006 A.A. Markov Anniversary Meeting
An international conference in honor of the 150th anniversary of the birth of A.A. Markov was held on the campus of College of Charleston SC.
Special sessions were devoted to a historical perspective of
Markov and his contemporaries and to the many important applications of
Markov chains in today's fast paced world. A.A. Markov Anniversary Meeting.
Ode to AA - A poem by A. N. Langville
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Transparencies from Authors' Talks
Markov and the creation of Markov chains.
[Photos of Markov and his Contemporaries]
A Krylov-based projection algorithm for solving the chemical master equation arising in the discrete modellin g of biological systems.
K. Burrage, M. Hegland, S. MacNamara and R.B. Sidje.
Modeling through Markov chains: Where is the risk?
Structured Markov chains in applied probability and numerical analysis.
On the generality of binary tree-like Markov chains.
K. Spaey , B. Van Houdt and C. Blondia.
Performance analysis of assembly systems.
M. Van Vuuren and I.J.B.F. Adan.
The dynamic analysis and design of a communication link with stationary and nonstationary arrivals.
W. Tian and H.G. Perros.
Bounds for the coupling time in queueing networks perfect simulation.
J.G. Dopper, B Gaujal and J-M. Vincent.
Analysis of Markov reward models with partial reward loss based on a time reverse approach.
G. Horvath and M. Telek.
Applications of Markov chains in Demography.
The five greatest applications of Markov chains.
P. Von Hilgers and A.N. Langville.
Bounding the mean cumulated reward up to absorption.
A.P. Couto da Silva and G. Rubino.
Increasing convex monotone Markov chains: Theory, algorithms and applications.
M. Ben Mamoun, A. Busic, J-M. Fourneau and N. Pekergin.
Polynomials of a stochastic matrix and strong stochastic bounds.
T. Dayar, J-M. Fourneau, N. Pekergin, J-M. Vincent.
Updating Markov Chains.
A.N. Langville and C.D. Meyer.
Product preconditioning for Markov chain problems.
M. Benzi and B. Ucar.
The bounding discrete phase--type method.
J-S. Tancrez and P. Semal.
Entrywise conditioning of the stationary vector for a Google--type matrix.
Analyzing Markov chains based on Kronecker products .
Structured stochastic modeling and performance analysis of a multiprocessor system.
I. Sbeity and B. Plateau.
Matrix and random walk methods in web analysis.
Asynchronous Parallel Solution of Markov Chains: Application to PageRank.
Ranking National Football League teams using Google's PageRank.
A. Govan and C.D. Meyer.
Gaussian Elimination for the Google PageRank Problem, Insights and Limitations.
J. Crowson and R. E. Funderlic.
An Investigation into New Approaches for Clustering Matrices.
B. Ball and C.J. Rodgers.