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1. Bayesian probability theory : : applications in the physical sciences ((ebook))
Wolfgang von der Linden, Graz University of Technology, Institute for Theoretical and Computational Physics, Graz, Austria, Volker Dose, Max Planck Institute for Plasma Physics, Garching, Germany, Udo von Toussaint, Max Planck Institute for Plasma Physics, Garching, Germany
出版情報:   1 online resource (xiii, 637 p.)
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2.

電子ブック
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2. Bayesian filtering and smoothing (: electronic bk)
Simo Särkkä
出版情報: Cambridge Core  1 online resource (xxii, 232 p.)
シリーズ名: Institute of Mathematical Statistics textbooks ; 3
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電子ブック
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3. Bayesian modeling and computation in Python (: electronic bk)
Osvaldo A. Martin, Ravin Kumar and Junpeng Lao
出版情報: Taylor & Francis eBooks  1 online resource (xxii, 398 p.)
シリーズ名: Texts in statistical science
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4.

電子ブック
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4. Statistical rethinking : a Bayesian course with examples in R and Stan. 2nd ed (: electronic bk)
Richard McElreath
出版情報: [Ann Arbor, Mich.] : ProQuest Ebook Central , [Boca Raton] : CRC Press, [20--]  1 online resource (xvii, 593 p.)
シリーズ名: Texts in statistical science
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5.

電子ブック
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5. Fundamentals of nonparametric Bayesian inference (: electronic bk)
Subhashis Ghosal, North Carolina State University, Aad van der Vaart, Leiden University
出版情報:   1 online resource (xxiv, 646 p.)
シリーズ名: Cambridge series in statistical and probabilistic mathematics ; 44
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電子ブック
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6. Practice of Bayesian probability theory in geotechnical engineering
Wan-Huan Zhou, Zhen-Yu Yin, Ka-Veng Yuen
出版情報: EBSCOhost  1 online resource (xxvii, 324 p.)
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7.

電子ブック
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7. Decision theory with a human face (: electronic bk)
Richard Bradley, London School of Economics and Political Science
出版情報:   1 online resource (xiv, 335 p.)
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8.

電子ブック
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8. Bayesian analysis of stochastic process models (: electronic bk)
David Rios Insua, Fabrizio Ruggeri, Michael P. Wiper
出版情報: [S.l.] : Wiley Online Library, [20--]  1 online resource (xiii, 290 p.)
シリーズ名: Wiley series in probability and mathematical statistics
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目次情報: 続きを見る
Preface
Stochastic Processes / 1：
Introduction / 1.1：
Key Concepts in Stochastic Processes / 1.2：
Main Classes of Stochastic Processes / 1.3：
Inference, Prediction and Decision Making / 1.4：
Discussion / 1.5：
Bayesian Analysis / 2：
Bayesian Statistics / 2.1：
Bayesian Decision Analysis / 2.3：
Bayesian Computation / 2.4：
Discrete Time Markov Chains / 2.5：
Important Markov Chain Models / 3.1：
Inference for First Order Chains / 3.3：
Special Topics / 3.4：
Case Study: Wind Directions at Gijon / 3.5：
Markov Decision Processes / 3.6：
Continuous Time Markov Chains and Extensions / 3.7：
Basic Setup and Results / 4.1：
Inference and Prediction for CTMCs / 4.3：
Case Study: Hardware Availability through CTMCs / 4.4：
Semi-Markovian Processes / 4.5：
Decision Making with Semi-Markovian Decision Processes / 4.6：
Poisson Processes and Extensions / 4.7：
Basics on Poisson Processes / 5.1：
Homogeneous Poisson Processes / 5.3：
Nonhomogeneous Poisson Processes / 5.4：
Compound Poisson Processes / 5.5：
Further Extensions of Poisson Processes / 5.6：
Case Study: Earthquake Occurrences / 5.7：
Continuous Time Continuous Space Processes / 5.8：
Gaussian Processes / 6.1：
Brownian Motion and Fractional Brownian Motion / 6.3：
Di®usions / 6.4：
Case Study: Prey-predator Systems / 6.5：
Queueing Analysis / 6.6：
Basic Queueing Concepts / 7.1：
The Main Queueing Models / 7.3：
Inference for Queueing Systems / 7.4：
Inference for M=M=1 Systems / 7.5：
Inference for Non Markovian Systems / 7.6：
Decision Problems in Queueing Systems / 7.7：
Case Study: Optimal Number of Beds in a Hospital / 7.8：
Reliability / 7.9：
Basic Reliability Concepts / 8.1：
Renewal Processes / 8.3：
Poisson Processes / 8.4：
Other Processes / 8.5：
Maintenance / 8.6：
Case Study: Gas Escapes / 8.7：
Discrete Event Simulation / 8.8：
Discrete Event Simulation Methods / 9.1：
A Bayesian View of DES / 9.3：
Case Study: A G=G=1 Queueing System / 9.4：
Bayesian Output Analysis / 9.5：
Simulation and Optimization / 9.6：
Risk Analysis / 9.7：
Risk Measures / 10.1：
Ruin Problems / 10.3：
Case Study: Ruin Probability Estimation / 10.4：
Main Distributions / 10.5：
Generating Functions and the Laplace-Stieltjes Transform / Appendix B：
Index
Preface
Stochastic Processes / 1：
Introduction / 1.1：
9.

電子ブック
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9. Bayesian statistical modelling. 2nd ed (: electronic bk)
Peter Congdon
出版情報: [S.l.] : Wiley Online Library, [20--]  1 online resource (xi, 573 p.)
シリーズ名: Wiley series in probability and mathematical statistics
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Preface
Introduction: The Bayesian Method, its Benefits and Implementation / Chapter 1：
Bayesian Model Choice, Comparison and Checking / Chapter 2：
The Major Densities and their Application / Chapter 3：
Normal Linear Regression, General Linear Models and Log-Linear Models / Chapter 4：
Hierarchical Priors for Pooling Strength and Overdispersed Regression Modelling / Chapter 5：
Discrete Mixture Priors / Chapter 6：
Multinomial and Ordinal Regression Models / Chapter 7：
Time Series Models / Chapter 8：
Modelling Spatial Dependencies / Chapter 9：
Nonlinear and Nonparametric Regression / Chapter 10：
Multilevel and Panel Data Models / Chapter 11：
Latent Variable and Structural Equation Models for Multivariate Data / Chapter 12：
Survival and Event History Analysis / Chapter 13：
Missing Data Models / Chapter 14：
Measurement Error, Seemingly Unrelated Regressions, and Simultaneous Equations / Chapter 15：
A Brief Guide to Using WINBUGS / Appendix 1：
Index
A Brief Guide to Using Winbugs
Preface
Introduction: The Bayesian Method, its Benefits and Implementation / Chapter 1：
Bayesian Model Choice, Comparison and Checking / Chapter 2：
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電子ブック
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10. Applied Bayesian modelling. 2nd ed (: electronic bk)
Peter Congdon
出版情報: [S.l.] : Wiley Online Library, [20--]  1 online resource (ix, 437 p.)
シリーズ名: Wiley series in probability and mathematical statistics
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11.

電子ブック
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11. Bayesian networks : a practical guide to applications (: electronic bk)
edited by Olivier Pourret, Patrick Naim, Bruce Marcot
出版情報: [S.l.] : Wiley Online Library, [20--]  1 online resource (xv, 428 p.)
シリーズ名: Statistics in practice
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Foreword
Preface
Introduction to Bayesian networks / 1：
Models / 1.1：
Probabilistic vs. deterministic models / 1.2：
Unconditional and conditional independence / 1.3：
Bayesian networks / 1.4：
Medical diagnosis / 2：
Bayesian networks in medicine / 2.1：
Context and history / 2.2：
Model construction / 2.3：
Inference / 2.4：
Model validation / 2.5：
Model use / 2.6：
Comparison to other approaches / 2.7：
Conclusions and perspectives / 2.8：
Clinical decision support / 3：
Introduction / 3.1：
Models and methodology / 3.2：
The Busselton network / 3.3：
The PROCAMnetwork / 3.4：
The PROCAMBusselton network / 3.5：
Evaluation / 3.6：
The clinical support tool: TakeHeartII / 3.7：
Conclusion / 3.8：
Complex genetic models / 4：
Historical perspectives / 4.1：
Complex traits / 4.3：
Bayesian networks to dissect complex traits / 4.4：
Applications / 4.5：
Future challenges / 4.6：
Crime risk factors analysis / 5：
Analysis of the factors affecting crime risk / 5.1：
Expert probabilities elicitation / 5.3：
Data preprocessing / 5.4：
A Bayesian network model / 5.5：
Results / 5.6：
Accuracy assessment / 5.7：
Conclusions / 5.8：
Spatial dynamics in the coastal region / 6：
An indicator-based analysis / 6.1：
The Bayesian network model / 6.3：
Inference problems in forensic science / 6.4：
Building Bayesian networks for inference / 7.1：
Applications of Bayesian networks in forensic science / 7.3：
Conservation of marbled murrelets in British Columbia / 7.4：
Context/history / 8.1：
Model calibration, validation and use / 8.2：
Conclusions/perspectives / 8.4：
Classifiers for modeling of mineral potential / 9：
Mineral potential mapping / 9.1：
Classifiers for mineral potential mapping / 9.2：
Bayesian network mapping of base metal deposit / 9.3：
Discussion / 9.4：
Student modeling / 9.5：
Probabilistic relational models / 10.1：
Probabilistic relational student model / 10.3：
Case study / 10.4：
Experimental evaluation / 10.5：
Conclusions and future directions / 10.6：
Sensor validation / 11：
The problem of sensor validation / 11.1：
Sensor validation algorithm / 11.3：
Gas turbines / 11.4：
Models learned and experimentation / 11.5：
Discussion and conclusion / 11.6：
An information retrieval system / 12：
Overview / 12.1：
Bayesian networks and information retrieval / 12.3：
Theoretical foundations / 12.4：
Building the information retrieval system / 12.5：
Reliability analysis of systems / 12.6：
Dynamic fault trees / 13.1：
Dynamic Bayesian networks / 13.3：
A case study: The Hypothetical Sprinkler System / 13.4：
Terrorism risk management / 13.5：
The Risk Influence Network / 14.1：
Software implementation / 14.3：
Site Profiler deployment / 14.4：
Credit-rating of companies / 14.5：
Naive Bayesian classifiers / 15.1：
Example of actual credit-ratings systems / 15.3：
Credit-rating data of Japanese companies / 15.4：
Numerical experiments / 15.5：
Performance comparison of classifiers / 15.6：
Clas / 15.7：
The PROCAM network
The PROCAM Busselton network
Spatial dynamics in France
Classification of Chilean wines
Experimental setup / 16.1：
Feature extraction methods / 16.3：
Classification results / 16.4：
Pavement and bridge management / 16.5：
Pavement management decisions / 17.1：
Bridge management / 17.3：
Bridge approach embankment - case study / 17.4：
Complex industrial process operation / 17.5：
A methodology for Root Cause Analysis / 18.1：
Pulp and paper application / 18.3：
The ABB Industrial IT platform / 18.4：
Probability of default for large corporates / 18.5：
BayesCredit / 19.1：
Model benchmarking / 19.4：
Benefits from technology and software / 19.5：
Risk management in robotics / 19.6：
DeepC / 20.1：
The ADVOCATE II architecture / 20.3：
Model development / 20.4：
Model usage and examples / 20.5：
Benefits from using probabilistic graphical models / 20.6：
Enhancing Human Cognition / 20.7：
Human foreknowledge in everyday settings / 21.1：
Machine foreknowledge / 21.3：
Current application and future research needs / 21.4：
An artificial intelligence perspective / 21.5：
A rational approach of knowledge / 22.2：
Bibliography / 22.3：
Index
Foreword
Preface
Introduction to Bayesian networks / 1：
12.

電子ブック
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12. Applied Bayesian modeling and causal inference from incomplete-data perspectives : an essential journey with Donald Rubin's statistical family (: electronic bk)
edited by Andrew Gelman, Xiao-Li Meng
出版情報: [S.l.] : Wiley Online Library, [20--]  1 online resource (xix, 407 pages)
シリーズ名: Wiley series in probability and mathematical statistics
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Preface
Casual inference and observational studies / I：
An overview of methods for causal inference from observational studies / Sander Greenland1：
Introduction / 1.1：
Approaches based on causal models / 1.2：
Canonical inference / 1.3：
Methodologic modeling / 1.4：
Conclusion / 1.5：
Matching in observational studies / Paul R. Rosenbaum2：
The role of matching in observational studies / 2.1：
Why match? / 2.2：
Two key issues: balance and structure / 2.3：
Additional issues / 2.4：
Estimating causal effects in nonexperimental studies / Rajeev Dehejia3：
Identifying and estimating the average treatment effect / 3.1：
The NSWdata / 3.3：
Propensity score estimates / 3.4：
Conclusions / 3.5：
Medication cost sharing and drug spending in Medicare / Alyce S. Adams4：
Methods / 4.1：
Results / 4.2：
Study limitations / 4.3：
Conclusions and policy implications / 4.4：
A comparison of experimental and observational data analyses / Jennifer L. Hill ; Jerome P. Reiter ; Elaine L. Zanutto5：
Experimental sample / 5.1：
Constructed observational study / 5.2：
Concluding remarks / 5.3：
Fixing broken experiments using the propensity score / Bruce Sacerdote6：
The lottery data / 6.1：
Estimating the propensity scores / 6.3：
The propensity score with continuous treatments / Keisuke Hirano ; Guido W. Imbens6.4：
The basic framework / 7.1：
Bias removal using the GPS / 7.3：
Estimation and inference / 7.4：
Application: the Imbens-Rubin-Sacerdote lottery sample / 7.5：
Causal inference with instrumental variables / Junni L. Zhang7.6：
Key assumptions for the LATE interpretation of the IV estimand / 8.1：
Estimating causal effects with IV / 8.3：
Some recent applications / 8.4：
Discussion / 8.5：
Principal stratification / Constantine E. Frangakis9：
Introduction: partially controlled studies / 9.1：
Examples of partially controlled studies / 9.2：
Estimands / 9.3：
Assumptions / 9.5：
Designs and polydesigns / 9.6：
Missing data modeling / II：
Nonresponse adjustment in government statistical agencies: constraints, inferential goals, and robustness issues / John L. Eltinge10：
Introduction: a wide spectrum of nonresponse adjustment efforts in government statistical agencies / 10.1：
Constraints / 10.2：
Complex estimand structures, inferential goals, and utility functions / 10.3：
Robustness / 10.4：
Closing remarks / 10.5：
Bridging across changes in classification systems / Nathaniel Schenker11：
Multiple imputation to achieve comparability of industry and occupation codes / 11.1：
Bridging the transition from single-race reporting to multiple-race reporting / 11.3：
Representing the Census undercount by multiple imputation of households / Alan M. Zaslavsky11.4：
Models / 12.1：
Inference / 12.3：
Simulation evaluations / 12.4：
Statistical disclosure techniques based on multiple imputation / Roderick J. A. Little ; Fang Liu ; Trivellore E. Raghunathan12.5：
Full synthesis / 13.1：
SMIKe and MIKe / 13.3：
Analysis of synthetic samples / 13.4：
An application / 13.5：
Designs producing balanced missing data: examples from the National Assessment of Educational Progress / Neal Thomas13.6：
Statistical methods in NAEP / 14.1：
Split and balanced designs for estimating population parameters / 14.3：
Maximum likelihood estimation / 14.4：
The role of secondary covariates / 14.5：
Propensity score estimation with missing data / Ralph B. D Agostino Jr14.6：
Notation / 15.1：
Applied example:March of Dimes data / 15.3：
Conclusion and future directions / 15.4：
Sensitivity to nonignorability in frequentist inference / Guoguang Ma ; Daniel F. Heitjan16：
Missing data in clinical trials / 16.1：
Ignorability and bias / 16.2：
A nonignorable selection model / 16.3：
Sensitivity of the mean and variance / 16.4：
Sensitivity of the power / 16.5：
Sensitivity of the coverage probability / 16.6：
An example / 16.7：
Statistical modeling and computation / 16.8：
Statistical modeling / 17：
The NSW data
Applied example: March of Dimes data / Ralph B. D'Agostino Jr.
Regression models / D. Michael Titterington17.1：
Latent-variable problems / 17.2：
Computation: non-Bayesian / 17.3：
Computation: Bayesian / 17.4：
Prospects for the future / 17.5：
Treatment effects in before-after data / Andrew Gelman18：
Default statistical models of treatment effects / 18.1：
Before-after correlation is typically larger for controls than for treated units / 18.2：
A class of models for varying treatment effects / 18.3：
Multimodality in mixture models and factor models / Eric Loken18.4：
Multimodality in mixture models / 19.1：
Multimodal posterior distributions in continuous latent variable models / 19.2：
Summary / 19.3：
Modeling the covariance and correlation matrix of repeated measures / W. John Boscardin ; Xiao Zhang20：
Modeling the covariance matrix / 20.1：
Modeling the correlation matrix / 20.3：
Modeling a mixed covariance-correlation matrix / 20.4：
Nonzero means and unbalanced data / 20.5：
Multivariate probit model / 20.6：
Example: covariance modeling / 20.7：
Example: mixed data / 20.8：
Robit regression: a simple robust alternative to logistic and probit regression / Chuanhai Liu21：
The robit model / 21.1：
Robustness of likelihood-based inference using logistic, probit, and robit regression models / 21.3：
Complete data for simple maximum likelihood estimation / 21.4：
Maximum likelihood estimation using EM-type algorithms / 21.5：
A numerical example / 21.6：
Using EM and data augmentation for the competing risks model / Radu V. Craiu ; Thierry Duchesne21.7：
The model / 22.1：
EM-based analysis / 22.3：
Bayesian analysis / 22.4：
Example / 22.5：
Discussion and further work / 22.6：
Mixed effects models and the EM algorithm / Florin Vaida ; Xiao-Li Meng ; Ronghui Xu23：
Binary regression with random effects / 23.1：
Proportional hazards mixed-effects models / 23.3：
The sampling/importance resampling algorithm / Kim-Hung Li24：
SIR algorithm / 24.1：
Selection of the pool size / 24.3：
Selection criterion of the importance sampling distribution / 24.4：
The resampling algorithms / 24.5：
Applied Bayesian inference / 24.6：
Whither applied Bayesian inference? / Bradley P. Carlin25：
Where we've been / 25.1：
Where we are / 25.2：
Where we're going / 25.3：
Efficient EM-type algorithms for fitting spectral lines in high-energy astrophysics / David A. van Dyk ; Taeyoung Park26：
Application-specific statistical methods / 26.1：
The Chandra X-ray observatory / 26.2：
Fitting narrow emission lines / 26.3：
Model checking and model selection / 26.4：
Improved predictions of lynx trappings using a biological model / Cavan Reilly ; Angelique Zeringue27：
The current best model / 27.1：
Biological models for predator prey systems / 27.3：
Some statistical models based on the Lotka-Volterra system / 27.4：
Computational aspects of posterior inference / 27.5：
Posterior predictive checks and model expansion / 27.6：
Prediction with the posterior mode / 27.7：
Record linkage using finite mixture models / Michael D. Larsen27.8：
Introduction to record linkage / 28.1：
Record linkage / 28.2：
Mixture models / 28.3：
Application / 28.4：
Analysis of linked files / 28.5：
Bayesian hierarchical record linkage / 28.6：
Identifying likely duplicates by record linkage in a survey of prostitutes / Thomas R. Belin ; Hemant Ishwaran ; Naihua Duan ; Sandra H. Berry ; David E. Kanouse28.7：
Concern about duplicates in an anonymous survey / 29.1：
General frameworks for record linkage / 29.2：
Estimating probabilities of duplication in the Los Angeles Women's Health Risk Study / 29.3：
Applying structural equation models with incomplete data / Hal S. Stern ; Yoonsook Jeon29.4：
Structural equation models / 30.1：
Bayesian inference for structural equation models / 30.2：
Iowa Youth and Families Project example / 30.3：
Summary and discussion / 30.4：
Perceptual scaling / Ying Nian Wu ; Cheng-En Guo ; Song Chun Zhu31：
Sparsity and minimax entropy / 31.1：
Complexity scaling law / 31.3：
Perceptibility scaling law / 31.4：
Texture = imperceptible structures / 31.5：
Perceptibility and sparsity / 31.6：
References
Index
Preface
Casual inference and observational studies / I：
An overview of methods for causal inference from observational studies / Sander Greenland1：
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