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1.

電子ブック
ＥＢ
1. 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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2.

電子ブック
ＥＢ
2. 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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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：
3.

電子ブック
ＥＢ
3. 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：
4.

電子ブック
ＥＢ
4. 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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5.

電子ブック
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5. 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：
6.

電子ブック
ＥＢ
6. 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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