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Bayesian analysis of stochastic process models / / David Rios Insua, Fabrizio Ruggeri, Michael P. Wiper
| Bayesian analysis of stochastic process models / / David Rios Insua, Fabrizio Ruggeri, Michael P. Wiper |
| Autore | Ríos Insua David <1964-> |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey, : Wiley, 2012 |
| Descrizione fisica | 1 online resource (316 p.) |
| Disciplina | 519.5/42 |
| Altri autori (Persone) |
WiperMichael P
RuggeriFabrizio |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Bayesian statistical decision theory
Stochastic processes |
| ISBN |
9786613619761
9781280589935 1280589930 9781118304037 1118304039 9780470975916 0470975911 9780470975923 047097592X |
| Classificazione | MAT029010 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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 Processes 259 8.6 Maintenance 262 8.7 Case Study: Gas Escapes 263 8.8 Discussion 271 9 Discrete Event Simulation 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: Ruin Probability Estimation 320 10.5 Discussion 327 Appendix A Main Distributions 337 Appendix B Generating Functions and the Laplace-Stieltjes Transform 347 Index. |
| Record Nr. | UNINA-9910141318903321 |
Ríos Insua David <1964->
|
||
| Hoboken, New Jersey, : Wiley, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian estimation and tracking [[electronic resource] ] : a practical guide / / Anton J. Haug
| Bayesian estimation and tracking [[electronic resource] ] : a practical guide / / Anton J. Haug |
| Autore | Haug Anton J. <1941-> |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, 2012 |
| Descrizione fisica | 1 online resource (397 p.) |
| Disciplina | 519.5/42 |
| Soggetto topico |
Bayesian statistical decision theory
Automatic tracking - Mathematics Estimation theory |
| ISBN |
1-280-68723-1
9786613664174 1-118-28780-0 1-118-28783-5 1-118-28779-7 |
| Classificazione | MAT029010 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | pt. 1. Preliminaries -- pt. 2. The Gaussian assumption : a family of Kalman filter estimators -- pt. 3. Monte Carlo methods -- pt. 4. Additional case studies. |
| Record Nr. | UNINA-9910133837003321 |
|
Haug Anton J. <1941->
|
||
| Hoboken, N.J., : Wiley, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian estimation and tracking : a practical guide / / Anton J. Haug
| Bayesian estimation and tracking : a practical guide / / Anton J. Haug |
| Autore | Haug Anton J. <1941-> |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, 2012 |
| Descrizione fisica | 1 online resource (397 p.) |
| Disciplina | 519.5/42 |
| Soggetto topico |
Bayesian statistical decision theory
Automatic tracking - Mathematics Estimation theory |
| ISBN |
9786613664174
9781280687235 1280687231 9781118287804 1118287800 9781118287835 1118287835 9781118287798 1118287797 |
| Classificazione | MAT029010 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | pt. 1. Preliminaries -- pt. 2. The Gaussian assumption : a family of Kalman filter estimators -- pt. 3. Monte Carlo methods -- pt. 4. Additional case studies. |
| Record Nr. | UNINA-9910829022603321 |
|
Haug Anton J. <1941->
|
||
| Hoboken, N.J., : Wiley, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian methods for management and business : pragmatic solutions for real problems / / Eugene D. Hahn
| Bayesian methods for management and business : pragmatic solutions for real problems / / Eugene D. Hahn |
| Autore | Hahn Eugene D. |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (787 p.) |
| Disciplina | 650.01/519542 |
| Soggetto topico |
Management - Statistical methods
Commercial statistics Bayesian statistical decision theory |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-118-93519-5 |
| Classificazione | MAT029010 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine generated contents note: 1 Introduction to Bayesian Methods 1 1.1 Bayesian Methods: An Aerial Survey 1 1.2 Bayes' Theorem 4 1.3 Bayes' Theorem and the Focus Group 6 1.4 The Flavors of Probability 9 1.5 Summary 12 1.6 Notation Introduced in This Chapter 12 2 A First Look at Bayesian Computation 13 2.1 Getting Started 13 2.2 Selecting the Likelihood Function 14 2.3 Selecting the Functional Form 18 2.4 Selecting the Prior 19 2.5 Finding the Normalizing Constant 20 2.6 Obtaining the Posterior 20 2.7 Communicating Findings 25 2.8 Predicting Future Outcomes 28 2.9 Summary 30 2.10 Exercises 31 2.11 Notation Introduced in This Chapter 32 3 Computer-Assisted Bayesian Computation 33 3.1 Getting Started 33 3.2 Random Number Sequences 34 3.3 Monte Carlo Integration 36 3.4 Monte Carlo Simulation for Inference 40 3.5 The Conjugate Normal Model 44 3.6 In Practice: The Conjugate Normal Model 50 3.7 Count Data and the Conjugate Poisson Model 57 3.8 Summary 61 3.9 Exercises 62 3.10 Notation Introduced in This Chapter 63 3.11 Appendix - In Detail: Finding Posterior Distributions for the Normal Model 63 4 MCMC and Regression Models 71 4.1 Introduction to Markov Chain Monte Carlo 71 4.2 Fundamentals of MCMC 73 4.3 Gibbs Sampling 75 4.4 Gibbs Sampling and the Simple Linear Regression Model 82 4.5 In Practice: The Simple Linear Regression Model 85 4.6 The Metropolis Algorithm 88 4.7 Hastings' Extension of the Metropolis Algorithm 97 4.8 Summary 102 4.9 Exercises 103 5 Estimating Bayesian Models with WinBUGS 105 5.1 An Introduction to WinBUGS 106 5.2 In Practice: A First WinBUGS Model 107 5.3 In Practice: Models for the Mean in WinBUGS 117 5.4 Examining the Prior with Sensitivity Analysis 125 5.5 In Practice: Examining Proportions in WinBUGS 136 5.6 Analysis of Variance Models 142 5.7 Higher-order ANOVA Models 155 5.8 Regression and ANCOVA Models in WinBUGS 163 5.9 Summary 171 5.10 Chapter Appendix: Exporting WinBUGS MCMC Output to R 171 5.11 Exercises 173 6 Assessing MCMC Performance in WinBUGS 175 6.1 Convergence Issues in MCMC Modeling 175 6.2 Output Diagnostics in WinBUGS 178 6.3 Reparameterizing to Improve Convergence 181 6.4 Number and Length of Chains 186 6.5 Metropolis-Hastings Acceptance Rates 197 6.6 Summary 199 6.7 Exercises 200 7 Model Checking and Model Comparison 203 7.1 Graphical Model Checking 203 7.2 Predictive Densities and Checking Model Assumptions 209 7.3 Variable Selection Methods 216 7.4 Bayes Factors and BIC 227 7.5 Deviance Information Criterion 234 7.6 Summary 241 7.7 Exercises 241 8 Hierarchical Models 243 8.1 Fundamentals of Hierarchical Models 243 8.2 The Random Coefficients Model 256 8.3 Hierarchical Models for Variance Terms 267 8.4 Functional Forms at Multiple Hierarchical Levels 273 8.5 In Detail: Modeling Covarying Hierarchical Terms 279 8.6 Summary 286 8.7 Exercises 286 8.8 Notation Introduced in This Chapter 288 9 Generalized Linear Models 289 9.1 Fundamentals of Generalized Linear Models 289 9.2 Count Data Models: Poisson Regression 292 9.3 Models for Binary Data: Logistic Regression 296 9.4 The Probit Model 303 9.5 In Detail: Multinomial Logistic Regression for Categorical Outcomes 306 9.6 Hierarchical Models for Count Data 314 9.7 Hierarchical Models for Binary Data 320 9.8 Summary 324 9.9 Exercises 325 9.10 Notation Introduced in This Chapter 327 10 Models for Difficult Data 329 10.1 Living with Outliers-Robust Regression Models 329 10.2 Handling Heteroscedasticity by Modeling Variance Parameters 340 10.3 Dealing with Missing Data 345 10.4 Types of Missing Data 349 10.5 Missing Covariate Data and Non-Normal Missing Data 357 10.6 Summary 358 10.7 Exercises 359 10.8 Notation Introduced in This Chapter 360 11 Introduction to Latent Variable Models 361 11.1 Not Seen but Felt 361 11.2 Latent Variable Models for Binary Data 362 11.3 Structural Break Models 366 11.4 In Detail: The Ordinal Probit Model 376 11.5 Summary 383 11.6 Exercises 383 A Common Statistical Distributions 385 Bibliography 389 Author Index 403 Subject Index 407 . |
| Record Nr. | UNINA-9910465440303321 |
|
Hahn Eugene D.
|
||
| Hoboken, New Jersey : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||