AI SearchBayesian analysis of stochastic process models / (Record no. 104021)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04340cam a2200265 i 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 120104s2012 enka b 001 0 eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780470744536 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | SDU |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | QA279 |
| Item number | R929 B 2012 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Ruggeri, Fabrizio. |
| 245 10 - TITLE STATEMENT | |
| Title | Bayesian analysis of stochastic process models / |
| Statement of responsibility, etc. | David Rios Insua, Department of Statistics and Operations Research Universidad Rey Juan Carlos, Madrid, Spain, Fabrizio Ruggeri, CNR-IMATI, Milan, Italy, Michael P. Wiper, Department of Statistics, Universidad Carlos III de Madrid, Spain. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 290 p : |
| Other physical details | illustrations |
| 505 8# - Formatted Contents Note | |
| Formatted contents 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, time homogeneous, Markov 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 FBM [Fractional Brownian Motion] 174 6.4 Diffusions 181 6.5 Case Study: Predator-prey 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 Bayesian 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 Processes 259 8.6 Maintenance 262 8.7 Case Study: Gas Escapes 263 8.8 Discussion 271 279 9.1 Introduction 279 9.2 Discrete Event Simulation Methods 280 9.3 A Bayesian View of DES 283 9.4 Case Study: A G/G/1 Queueing System 286 9.5 Bayesian Output Analysis 288 9.6 Simulation and Optimization 292 9.7 Discussion 294 10 Risk Analysis 301 10.1 Introduction 301 10.2 Risk Measures 302 10.3 Ruin Problems 316 10.4 Case Study: Estimation of finite-time ruin probabilities in the Sparre Andersen model 320 10.5 Discussion 327 Appendix A Main Distributions 337 Appendix B Generating Functions and the Laplace-Stieltjes Transform 347 Index. |
| 505 8# - Formatted Contents Note | |
| Formatted contents note | HKBU library |
| 518 ## - DATE/TIME AND PLACE OF AN EVENT NOTE | |
| DATE/TIME AND PLACE OF AN EVENT NOTE | YT2025 M10 |
| 520 ## - SUMMARY | |
| Summary | "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"-- |
| 520 ## - SUMMARY | |
| Summary | "A unique book on Bayesian analyses of stochastic process based models"-- |
| 650 #0 - SUBJECT | |
| Topical term | Bayesian statistical decision theory. |
| 650 #0 - SUBJECT | |
| Topical term | Stochastic processes. |
| 650 #7 - SUBJECT | |
| Topical term | MATHEMATICS / Probability & Statistics / Bayesian Analysis. |
| 700 1# - PERSONAL NAME | |
| Personal name | Wiper, Michael P. |
| 700 1# - PERSONAL NAME | |
| Personal name | Ríos Insua, David, |
| Dates associated with a name | 1964- |
| 900 ## - Accession Number | |
| Accession Number | = C.1 SDU |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | English Books |
| Source of classification or shelving scheme | Library of Congress Classification |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| -- | 189351 |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Full call number | Barcode | Date last seen | Copy number | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Library of Congress Classification | Available for Loans | General Books | MATRIX Library | MATRIX Library | General Eng/FL.3 | 21/10/2025 | Donation | QA279 R929 B 2012 | 1000377816 | 21/10/2025 | C.1 | 21/10/2025 | English Books |