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Analytical and stochastic modelling techniques and applications28th International Conference, ASMTA 2024, Venice, Italy, June 14, 2024 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Analytical and stochastic modelling techniques and applicationsedited by Arnaud Devos, András Horváth, Sabina Rossi.
Reminder of title:	28th International Conference, ASMTA 2024, Venice, Italy, June 14, 2024 : proceedings /
remainder title:	ASMTA 2024
other author:	Devos, Arnaud.
corporate name:
Published:	Cham :Springer Nature Switzerland :2025.
Description:	ix, 165 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Computer science
Online resource:	https://doi.org/10.1007/978-3-031-70753-7
ISBN:	9783031707537$q(electronic bk.)

Analytical and stochastic modelling techniques and applications28th International Conference, ASMTA 2024, Venice, Italy, June 14, 2024 : proceedings /
Analytical and stochastic modelling techniques and applications28th International Conference, ASMTA 2024, Venice, Italy, June 14, 2024 : proceedings /[electronic resource] :ASMTA 2024edited by Arnaud Devos, András Horváth, Sabina Rossi. - Cham :Springer Nature Switzerland :2025. - ix, 165 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,148261611-3349 ;. - Lecture notes in computer science ;4891..

Markov chain aggregation with error bounds on transient distributions. -- Strong Aggregation in the Stochastic matching model with Random Discipline. -- Optimal Allocation of Tasks to Networked Computing Facilities. -- Revenue Management for Parallel Services with Fully Observable Queues. -- Deep reinforcement learning for weakly coupled MDP's with continuous actions. -- A lazy abstraction algorithm for Markov decision processes: theory and initial evaluation. -- Queueing Analysis of an Ensemble Machine Learning System. -- Analysis of load balancing prioritization for heterogeneous M/M/c/K server clusters in the stationary mean-field regime. -- An algebraic proof of the relation of Markov fluid queues and QBD processes. -- Stability of the Multiserver Job Queuing Model with Infinite Resources.

This book constitutes the refereed proceedings of the 28th International Conference on Analytical and Stochastic Modelling Techniques and Applications, ASMTA 2024, held in Venice, Italy, on June 14, 2024. The 10 full papers presented were carefully reviewed and selected from 14 submissions. These papers covered a wide range of topics in analytical and stochastic modeling techniques and their applications.

ISBN: 9783031707537$q(electronic bk.)

Standard No.: 10.1007/978-3-031-70753-7doiSubjects--Topical Terms:

252805
Computer science

LC Class. No.: QA76.9.E94

Dewey Class. No.: 004.24

Analytical and stochastic modelling techniques and applications28th International Conference, ASMTA 2024, Venice, Italy, June 14, 2024 : proceedings /
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505 0 $a Markov chain aggregation with error bounds on transient distributions. -- Strong Aggregation in the Stochastic matching model with Random Discipline. -- Optimal Allocation of Tasks to Networked Computing Facilities. -- Revenue Management for Parallel Services with Fully Observable Queues. -- Deep reinforcement learning for weakly coupled MDP's with continuous actions. -- A lazy abstraction algorithm for Markov decision processes: theory and initial evaluation. -- Queueing Analysis of an Ensemble Machine Learning System. -- Analysis of load balancing prioritization for heterogeneous M/M/c/K server clusters in the stationary mean-field regime. -- An algebraic proof of the relation of Markov fluid queues and QBD processes. -- Stability of the Multiserver Job Queuing Model with Infinite Resources.
520 $a This book constitutes the refereed proceedings of the 28th International Conference on Analytical and Stochastic Modelling Techniques and Applications, ASMTA 2024, held in Venice, Italy, on June 14, 2024. The 10 full papers presented were carefully reviewed and selected from 14 submissions. These papers covered a wide range of topics in analytical and stochastic modeling techniques and their applications.
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