Advances in algorithmic methods for stochastic models
Read Online
Share

Advances in algorithmic methods for stochastic models proceedings of the 3rd International Conference on Matrix Analytic Methods by International Conference on Matrix Analytic Methods (3rd 2000 Leuven, Belgium)

  • 281 Want to read
  • ·
  • 13 Currently reading

Published by Notable Publications in Neshanic Station, NJ .
Written in English

Subjects:

  • Markov processes -- Congresses.,
  • Queuing theory -- Congresses.,
  • Matrices -- Congresses.,
  • Stochastic processes -- Congresses.

Book details:

Edition Notes

Other titlesProceedings of the 3rd International Conference on Matrix Analytic Methods
Statementedited by Guy Latouche and Peter Taylor.
GenreCongresses.
ContributionsLatouche, G., Taylor, Peter
Classifications
LC ClassificationsQA274.7 .I572 2000
The Physical Object
Paginationxi, 433 p.. :
Number of Pages433
ID Numbers
Open LibraryOL3703849M
ISBN 100966584716
LC Control Number2003271491
OCLC/WorldCa46940797

Download Advances in algorithmic methods for stochastic models

PDF EPUB FB2 MOBI RTF

Recent Advances and Trends in Nonparametric Statistics. Book • Edited by: Select Stochastic Multiresolution Models for Turbulence. wavelets and nonlinear smoothers, graphical methods, data mining, bioinformatics, as well as the more recent algorithmic approaches such as bagging and boosting. This volume is a collection of short. The only forum on the theoretical, algorithmic and methodological aspects of matrix-analytic and related methods in stochastic models, and their application across various fields This area of mathematics and its applications have grown and advanced tremendously over the past few years from the previous original and early developments in the area. Abstract. Algorithms for the S-I-R epidemic with an initial population with m infectives and n susceptibles are examined. We propose efficient algorithms for the distributions of the total and the maximum size of the epidemic, and for the joint Cited by: The second part of the book is exclusively dedicated to algorithmic trading models. I found useful to support this book with Continuous-time Stochastic Control and Optimization with Financial Applications (Stochastic Modelling and Applied Probability) by Pham, which provides an in-depth mathematical structure where the reader may need it/5(34).

In this dissertation, we present some of the recent advances made in solving two-stage stochastic linear programming problems of large size and complexity. Decomposition and sampling are two fundamental components of techniques to solve stochastic optimization problems. We describe improvements to.   This volume presents the most recent applied and methodological issues in stochastic modeling and data analysis. The contributions cover various fields such as stochastic processes and applications, data analysis methods and techniques, Bayesian methods, biostatistics, econometrics, sampling, linear and nonlinear models, networks and queues. "Constructive Computation in Stochastic Models with Applications: The RG-Factorizations" provides a unified, constructive and algorithmic framework for numerical computation of many practical stochastic summarizes recent important advances in computational study of stochastic models from several crucial directions, such as stationary computation, transient Author: Quan-Lin Li. Fundamentals of Matrix-Analytic Methods, Springer, New York, (The book has been published as of August 1, ) Matrix-Analytic Methods in Stochastic Models: Proceedings of the Ninth International Conference on Matrix-Analytic Methods in Stochastic Models. Co-editors: He, Qi-Ming, Gabor Horvath, and Miklos Telek.

  The field of applied probability has changed profoundly in the past twenty years. The development of computational methods has greatly contributed to a better understanding of the theory. A First Course in Stochastic Models provides a self-contained introduction to the theory and applications of stochastic models. Emphasis is placed on establishing the 3/5(1). The book also covers the fundamentals of statistical parameter estimation, Wiener and Kalman filtering, convexity and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed. Get this from a library! Constructive computation in stochastic models with applications: the RG-factorization. [Quan-Lin Li] -- Constructive Computation in Stochastic Models with Applications The RG-Factorizations provides a unified, constructive and algorithmic framework for numerical computation of many practical stochastic. Get this from a library! Constructive computation in stochastic models with applications: the RG-factorization. [Quan-Lin Li] -- "Constructive Computation in Stochastic Models with Applications: The RG-Factorizations" provides a unified, constructive and algorithmic framework for numerical computation of many practical.