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: Bayesian analysis of stochastic process models
  name:
    name part: Ruggeri, Fabrizio.
    role:
      role term: creator
  name:
    name part: Wiper, Michael P.
  name:
    name part: Ríos Insua, David
    name part: 1964-
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: nju
    date issued: 2012
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: "This book provides analysis of stochastic processes from a Bayesian perspective with coverage of the main classes of stochastic processing, including modeling, computational, inference, prediction, decision-making and important applied models based on stochastic processes. In offers an introduction of MCMC and other statistical computing machinery that have pushed forward advances in Bayesian methodology. Addressing the growing interest for Bayesian analysis of more complex models, based on stochastic processes, this book aims to unite scattered information into one comprehensive and reliable volume"--
  abstract: "A unique book on Bayesian analyses of stochastic process based models"--
  table of contents: Front Matter -- Basic Concepts and Tools. Stochastic Processes -- Bayesian Analysis -- Models. Discrete Time Markov Chains and Extensions -- Continuous Time Markov Chains and Extensions -- Poisson Processes and Extensions -- Continuous Time Continuous Space Processes -- Applications. Queueing Analysis -- Reliability -- Discrete Event Simulation -- Risk Analysis -- Appendix A: Main Distributions -- Appendix B: Generating Functions and the Laplace₆Stieltjes Transform -- Index -- Wiley Series in Probability and Statistics.
  note: Fabrizio Ruggeri, Michael P. Wiper, David Rios Insua.
  note: Machine generated contents note: Preface 1 Stochastic Processes 11 1.1 Introduction 11 1.2 Key Concepts in Stochastic Processes 11 1.3 Main Classes of Stochastic Processes 16 1.4 Inference, Prediction and Decision Making 21 1.5 Discussion 23 2 Bayesian Analysis 27 2.1 Introduction 27 2.2 Bayesian Statistics 28 2.3 Bayesian Decision Analysis 37 2.4 Bayesian Computation 39 2.5 Discussion 51 3 Discrete Time Markov Chains 61 3.1 Introduction 61 3.2 Important Markov Chain Models 62 3.3 Inference for First Order Chains 66 3.4 Special Topics 76 3.5 Case Study: Wind Directions at Gij́on 87 3.6 Markov Decision Processes 94 3.7 Discussion 97 4 Continuous Time Markov Chains and Extensions 105 4.1 Introduction 105 4.2 Basic Setup and Results 106 4.3 Inference and Prediction for CTMCs 108 4.4 Case Study: Hardware Availability through CTMCs 112 4.5 Semi-Markovian Processes 118 4.6 Decision Making with Semi-Markovian Decision Processes 122 4.7 Discussion 128 5 Poisson Processes and Extensions 133 5.1 Introduction 133 5.2 Basics on Poisson Processes 134 5.3 Homogeneous Poisson Processes 138 5.4 Nonhomogeneous Poisson Processes 147 5.5 Compound Poisson Processes 153 5.6 Further Extensions of Poisson Processes 154 5.7 Case Study: Earthquake Occurrences 157 5.8 Discussion 162 6 Continuous Time Continuous Space Processes 169 6.1 Introduction 169 6.2 Gaussian Processes 170 6.3 Brownian Motion and Fractional Brownian Motion 174 6.4 Dilusions 181 6.5 Case Study: Prey-predator Systems 184 6.6 Discussion 190 7 Queueing Analysis 201 7.1 Introduction 201 7.2 Basic Queueing Concepts 201 7.3 The Main Queueing Models 204 7.4 Inference for Queueing Systems 208 7.5 Inference for M=M=1 Systems 209 7.6 Inference for Non Markovian Systems 220 7.7 Decision Problems in Queueing Systems 229 7.8 Case Study: Optimal Number of Beds in a Hospital 230 7.9 Discussion 235 8 Reliability 245 8.1 Introduction 245 8.2 Basic Reliability Concepts 246 8.3 Renewal Processes 249 8.4 Poisson Processes 251 8.5 Other Proc…
  note: Includes bibliographical references and index.
  subject:
    topic: Bayesian statistical decision theory
  subject:
    topic: Stochastic processes
  subject:
    topic: MATHEMATICS
    topic: Probability & Statistics
    topic: Bayesian Analysis
  subject:
    topic: Bayesian statistical decision theory
  subject:
    topic: Stochastic processes
  classification: QA279.5
  classification: 519.5/42
  classification: MAT029010
  related item:
    title info:
      title: Bayesian analysis of stochastic process models
    name:
      name part: Ruggeri, Fabrizio.
    origin info:
      publisher: Hoboken, New Jersey : Wiley, 2012
    identifier: (DLC) 2012000092
  identifier: 9780470975923
  identifier: 047097592X
  identifier: 9781118304037
  identifier: 1118304039
  identifier: 9781118304204
  identifier: 1118304209
  identifier: 9780470975916
  identifier: 0470975911
  identifier: 9780470744536
  identifier: 2012004782
  identifier: 10.1002/9780470975916 Wiley InterScience
  identifier: https://doi.org/10.1002/9780470975916
  record info:
    record content source: DLC
    record creation date: 120202
    record change date: 20170124070446.9
    record identifier: ocn775099472
    language of cataloging:
      language term: eng