rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Handbook of Markov Decision Processes
    sub title: Methods and Applications
  name:
    name part: Feinberg, Eugene A.
    role:
      role term: creator
  name:
    name part: Shwartz, Adam.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: xxu
    date issued: 2002
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (viii, 565 pages)
  abstract: The theory of Markov Decision Processes - also known under several other names including sequential stochastic optimization, discrete-time stochastic control, and stochastic dynamic programming - studies sequential optimization of discrete time stochastic systems. Fundamentally, this is a methodology that examines and analyzes a discrete-time stochastic system whose transition mechanism can be controlled over time. Each control policy defines the stochastic process and values of objective functions associated with this process. Its objective is to select a "good" control policy. In real life, decisions that humans and computers make on all levels usually have two types of impacts: (i) they cost or save time, money, or other resources, or they bring revenues, as well as (ii) they have an impact on the future, by influencing the dynamics. In many situations, decisions with the largest immediate profit may not be good in view of future events. Markov Decision Processes (MDPs) model this paradigm and provide results on the structure and existence of good policies and on methods for their calculations. MDPs are attractive to many researchers because they are important both from the practical and the intellectual points of view. MDPs provide tools for the solution of important real-life problems. In particular, many business and engineering applications use MDP models. Analysis of various problems arising in MDPs leads to a large variety of interesting mathematical and computational problems. Accordingly, the Handbook of Markov Decision Processes is split into three parts: Part I deals with models with finite state and action spaces and Part II deals with infinite state problems, and Part III examines specific applications. Individual chapters are written by leading experts on the subject.
  note: edited by Eugene A. Feinberg, Adam Shwartz.
  note: Electronic resource.
  subject:
    topic: Economics
  subject:
    topic: Mathematical optimization
  subject:
    topic: Distribution (Probability theory)
  subject:
    topic: Mechanical engineering
  subject:
    topic: Operations research
  subject:
    topic: Distribution (Probability theory)
  subject:
    topic: Economics
  subject:
    topic: Mathematical optimization
  subject:
    topic: Mechanical engineering
  subject:
    topic: Operations research
  classification: HD30.23
  classification: 658.40301
  related item:
    title info:
      title: International series in operations research & management science ; 40
  identifier: 9781461508052 (electronic bk.)
  identifier: 1461508053 (electronic bk.)
  identifier: 9781461352488
  identifier: 1461352487
  identifier: 1461508053
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: AU@
    record creation date: 121227
    record change date: 20260420212513.8
    record identifier: in00003558441
    language of cataloging:
      language term: eng