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Analytical and Stochastic Modelling Techniques and Applications: 28th International Conference, ASMTA 2024, Venice, Italy, June 14, 2024, Proceedings 2024 ed. [Mīkstie vāki]

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  • Formāts: Paperback / softback, 165 pages, height x width: 235x155 mm, 39 Illustrations, color; 11 Illustrations, black and white; X, 165 p. 50 illus., 39 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 14826
  • Izdošanas datums: 20-Oct-2024
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031707524
  • ISBN-13: 9783031707520
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Analytical and Stochastic Modelling Techniques and Applications: 28th International Conference, ASMTA 2024, Venice, Italy, June 14, 2024, Proceedings 2024 ed.Palielināt
  • Formāts: Paperback / softback, 165 pages, height x width: 235x155 mm, 39 Illustrations, color; 11 Illustrations, black and white; X, 165 p. 50 illus., 39 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 14826
  • Izdošanas datums: 20-Oct-2024
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031707524
  • ISBN-13: 9783031707520
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031707520.html
Keywords:
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.
.- 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 MDPs 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.