1. Bayesian expert resolution [1971]
 Morris, Peter Alan.
 1971.
 Description
 Book — xiii, 207 l.
 Online
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3781 1971 .M  Inlibrary use 
 Cashen, Jerry Joseph.
 1971.
 Description
 Book — vi, 75 l.
 Online
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3781 1971 .C  Inlibrary use 
 Kriz, Jürgen.
 Wien : Institut für höhere Studien, 1968.
 Description
 Book — 36 l. : ill.
 Online
SAL3 (offcampus storage)
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See linked record to request items bound together  
H62 .A1 I58 NO.17  Available 
4. Bayesian inference and computation in reliability and survival analysis [electronic resource] [2022]
 Cham, Switzerland : Springer, 2022.
 Description
 Book — 1 online resource.
 Summary

 1. A Bayesian Approach for Stepstress Accelerated Lifetests for Oneshot Devices under Exponential Distributions.
 2. Bayesian Estimation of Stressstrength Parameter for MoranDownton Bivariate Exponential Distribution under Progressive TypeII Censoring.
 3. Bayesian Computation in A BirnbaumSaunders Reliability Model with Applications to Fatigue Data.
 4. A Competing Risks Model Based on A Twoparameter Exponential Family Distribution under Progressive TypeII Censoring.
 5. Bayesian Computations for Reliability Analysis in Dynamic Environments.
 6. Bayesian Analysis of Stochastic Processes in Reliability.
 7. Bayesian Analysis of A New Bivariate Wiener Degradation Process.
 8. Bayesian Estimation for Bivariate Gamma Processes with Copula.
 9. Review of Statistical Treatment for Oncology Dose Escalation Trial with Prolonged Evaluation Window or Fast Enrollment.
 10. A Bayesian Approach for the Analysis of Tumorigenicity Data from Sacrificial Experiments under Weibull Lifetimes.
 11. Bayesian Sensitivity Analysis in Survival and Longitudinal Trial with Missing Data.
 12. Bayesian Analysis for Clustered Data under A Semicompeting Risks Framework.
 13. Survival Analysis for the Inverse Gaussian Distribution: Natural Conjugate and Jeffrey's Priors.
 14. Bayesian Inferences for Panel Count Data and Intervalcensored Data with Nonparametric Modeling of the Baseline Functions.
 15. Bayesian Approach for Intervalcensored Survival Data with Timevarying Coefficients.
 16. Bayesian Approach for Jointmodeling Longitudinal Data and Survival Data Simultaneously in Public Health Studies.
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 Heard, Nicholas, author.
 Cham, Switzerland : Springer, 2021.
 Description
 Book — 1 online resource (xii, 169 pages) : illustrations (some color) Digital: text file.PDF.
 Summary

 Uncertainty and Decisions. Prior and Likelihood Representation. Graphical Modeling. Parametric Models. Computational Inference. Bayesian Software Packages. Model choice. Linear Models. Nonparametric Models. Nonparametric Regression. Clustering and Latent Factor Models. Conjugate Parametric Models.
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 Miyawaki, Koji, author.
 Singapore : Springer, [2019]
 Description
 Book — 1 online resource (120 pages) Digital: text file.PDF.
 Summary

 1. Introduction.
 2. Demand under Increasing Block Rate Pricing.
 3. Demand under Decreasing Block Rate Pricing.
 4. Extensions to Panel Data.
 5. Extensions to Areal Data.
 6. Block Normal Simulator.
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 Donovan, Therese M. (Therese Marie), author.
 Oxford : Oxford University Press USA  OSO, 2019.
 Description
 Book — 1 online resource.
 Summary

 Section 1 Basics of Probability 1: Introduction to Probability 2: Joint, Marginal, and Conditional Probability
 Section 2 Bayes' Theorem and Bayesian Inference 3: Bayes' Theorem 4: Bayesian Inference 5: The Author Problem  Bayesian Inference with Two Hypotheses 6: The Birthday Problem: Bayesian Inference with Multiple Discrete Hypotheses 7: The Portrait Problem: Bayesian Inference with Joint Likelihood
 Section 3 Probability Functions 8: Probability Mass Functions 9: Probability Density Functions
 Section 4 Bayesian Conjugates 10: The White House Problem: The BetaBinomial Conjugate 11: The Shark Attack Problem: The GammaPoisson Conjugate 12: The Maple Syrup Problem: The NormalNormal Conjugate
 Section 5 Monte Carlo Markov Chains (MCMC) 13: The Shark Attack Problem Revisited: MCMC with the Metropolis Algorithm 14: MCMC Diagnostic Approaches 15: The White House Problem Revisited: MCMC with the MetropolisHastings Algorithm 16: The Maple Syrup Problem Revisited: MCMC with Gibbs Sampling
 Section 6 Applications 17: The Survivor Problem: Simple Linear Regression with MCMC 18: The Survivor Problem Continued: Introduction to Bayesian Model Selection 19: The Lorax Problem: Introduction to Bayesian Networks 20: The Onceler Problem: Introduction to Decision Trees Appendices
 Appendix 1: The BetaBinomial Conjugate Solution
 Appendix 2: The GammaPoisson Conjugate Solution
 Appendix 3: The NormalNormal Conjugate Solution
 Appendix 4: Conjugate Solutions for Simple Linear Regression
 Appendix 5: The Standardization of Regression Data.
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 Watanabe, Sumio, 1959 author.
 Boca Raton, FL : CRC Press, Taylor & Francis Group, [2018]
 Description
 Book — 1 online resource.
 Summary

 Definition of Bayesian Statistics
 Bayesian Statistics
 Probability distribution
 True Distribution
 Statistical model, prior, and posterior
 Examples of Posterior Distributions
 Estimation and Generalization
 Marginal Likelihood or Partition Function
 Conditional Independent Cases
 Statistical Models
 Normal Distribution
 Multinomial Distribution
 Linear regression
 Neural Network
 Finite Normal Mixture
 Nonparametric Mixture
 Basic Formula of Bayesian Observables
 Formal Relation between True and Model
 Normalized Observables
 Cumulant Generating Functions
 Basic Bayesian Theory
 Regular Posterior Distribution
 Division of Partition Function
 Asymptotic Free Energy
 Asymptotic Losses
 Proof of Asymptotic Expansions
 Point Estimators
 Standard Posterior Distribution
 Standard Form
 State Density Function
 Asymptotic Free Energy
 Renormalized Posterior Distribution
 Conditionally Independent Case
 General Posterior Distribution
 Bayesian Decomposition
 Resolution of Singularities
 General Asymptotic Theory
 Maximum A Posteriori Method
 Markov Chain Monte Carlo
 Metropolis Method
 Basic Metropolis Method
 Hamiltonian Monte Carlo
 Parallel Tempering
 Gibbs Sampler
 Gibbs Sampler for Normal Mixture
 Nonparametric Bayesian Sampler
 Numerical Approximation of Bayesian Observables
 Generalization and Cross Validation Losses
 Numerical Free Energy
 Information Criteria
 Model Selection
 Criteria for Generalization Loss
 Comparison of ISCV with WAIC
 Criteria for Free Energy
 Discussion for Model Selection
 Hyperparameter Optimization
 Criteria for Generalization Loss
 Criterion for Free energy
 Discussion for Hyperparameter Optimization
 Topics in Bayesian Statistics
 Formal Optimality
 Bayesian Hypothesis Test
 Bayesian Model Comparison
 Phase Transition
 Discovery Process
 Hierarchical Bayes
 Basic Probability Theory
 Delta Function
 KullbackLeibler Distance
 Probability Space
 Empirical Process
 Convergence of Expected Values
 Mixture by Dirichlet Process.
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 Oxford : Oxford University Press, 2018
 Description
 Book — 1 online resource (xxxiv, 889 pages) : illustrations (black and white)
 Summary

 Flexible Bayes Regression of Epidemiologic Data / David Dunson
 Bayesian Modelling of Train Doors Reliability / Antonio Pievatolo, Fabrizio Ruggeri
 Analysis of Economic Data With Multiscale Spatiotemporal Models / Marco Ferreira, Adelmo Bertoldey, Scott Holan
 Extracting S&P500 and NASDAQ Volatility: The Credit Crisis of 20072008 / Hedibert Lopes, Nicholas Polson
 Futures Markets, Bayesian Forecasting, and Risk Modeling / José Mario Quintana, Carlos Carvalho, James Scott, Thomas Costigliola
 The New Macroeconometrics: A Bayesian Approach / Jesús FernándezVillaverde, Pablo GuerrónQuintana, Juan RubioRamírez
 Assessing The Probability of Rare Climate Events / Peter Challenor, Doug McNeall, James Gattiker
 Models for Demography of Plant Populations / James S. Clark, Dave Bell, Michael Dietze, Michelle Hersh, Ines Ibanez, Shannon LaDeau, Sean McMahon, Jessica Metcalf, Emily Moran, Luke Pangle, Mike Wolosin
 Combining Monitoring Data and Computer Model Output in Assessing Environmental Exposure / Alan Gelfand, Sujit K. Sahu
 Indirect Elicitation From Ecological Experts: From Methods and Software to Habitat Modelling and RockWallabies / Samantha Low Choy, Justine Murray, Allan James, Kerrie Mengersen
 Characterizing the Uncertainty of Climate Change Projections Using Hierarchical Models / Claudia Tebaldi, Richard Smith
 Bayesian Modelling for Matching and Alignment of Biomolecules / Peter Green, Kanti Mardia, Vysaul Nyirongo, Yann Ruffieux
 Volatility in Prediction Markets: A Measure of Information Flow in Political Campaigns / Carlos Carvalho, Jill Rickershauser
 Paternity Testing Allowing for Uncertain Mutation Rates / A. Philip Dawid, Julia Mortera, Paola Vicard
 Bayesian Analysis in Item Response Theory Applied to a Largescale Educational Assessment / Dani Gamerman, Tufi M. Soares, Flávio Gonçalves
 Sequential Multilocation Auditing and the New York Food Stamps Program / Karl W. Heiner, Marc Kennedy, Anthony O'Hagan
 Bayesian Causal Inference: Approaches to Estimating the Effect of Treating Hospital Type on Cancer Survival in Sweden Using Principal Stratification / Donald Rubin, Xiaoqin Wang, Li Yin, Elizabeth Zell
 Bayesian Statistical Methods for Audio and Music Processing / A. Taylan Cemgil, Simon Godsill, Paul Peeling, Nick Whiteley
 Combining Simulations and Physical Observations to Estimate Cosmological Parameters / Dave Higdon, Katrin Heitmann, Charles Nakhleh, Salman Habib
 Probabilistic Grammars and Hierarchical Dirichlet Processes / Percy Liang, Michael Jordan, Dan Klein
 Designing and Analyzing a Circuit Device Experiment Using Treed Gaussian Processes / Herbert K.H. Lee, Matthew Taddy, Robert Gramacy, Genetha Gray
 Multistate Models for Mental Fatigue / Raquel Prado
 Bayesian Approaches to Aspects of the Vioxx Trials: Nonignorable Dropout and Sequential MetaAnalysis / Jerry Cheng, David Madigan
 Sensitivity Analysis in Microbial Risk Assessment: Verocytotoxigenic E.coli O157 in FarmPasteurised Milk / Jeremy E. Oakley, Helen E. Clough
 Mapping Malaria in the Amazon Rain Forest: a SpatioTemporal Mixture Model / Alexandra Schmidt, Jennifer Hoeting, João Batista M. Pereira, Pedro Paulo Vieira
 TransStudy Projection of Genomic Biomarkers in Analysis of Oncogene Deregulation and Breast Cancer / Dan Merl, Joseph Lucas, Joseph Nevins, Haige Shen, Mike West
 Linking Systems Biology Models to Data: a Stochastic Kinetic Model of p53 Oscillations / Daniel A. Henderson, R.J. Boys, Carole J. Proctor, Darren J. Wilkinson
 Bayesian Analysis and Decisions in Nuclear Power Plant Maintenance / Elmira Popova, David Morton, Paul Damien, Tim Hanson
 Bayes Linear Uncertainty Analysis for Oil Reservoirs Based on Multiscale Computer Experiments / Jonathan A. Cumming, Michael Goldstein
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10. Empirical Bayes Methods with Applications [2017]
 Maritz, J. S., author.
 Second edition  Boca Raton, FL : CRC Press, 2017
 Description
 Book — 1 online resource (296 pages)
 Summary

 1. Introduction to Bayes and Empirical Bayes methods
 2. Estimation of the Prior Distribution
 3. Empirical Bayes Point Estimation
 4. Empirical Bayes Point Estimation: Vector parameters
 5. Testing of Hypotheses
 6. Bayes and Empirical Bayes interval Estimation
 7. Alternatives to Empirical Bayes
 8. Applications of EB Methods.
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11. Introduction to Bayesian statistics [2017]
 Bolstad, William M., 1943 author.
 Third edition.  Hoboken, New Jersey : John Wiley & Sons, Inc., [2017]
 Description
 Book — xvi, 601 pages : illustrations ; 25 cm
 Summary

 Preface xiii 1 Introduction to Statistical Science 1 1.1 The Scientic Method: A Process for Learning 3 1.2 The Role of Statistics in the Scientic Method 5 1.3 Main Approaches to Statistics 5 1.4 Purpose and Organization of This Text 8 2 Scientic Data Gathering 13 2.1 Sampling from a Real Population 14 2.2 Observational Studies and Designed Experiments 17 Monte Carlo Exercises 23 3 Displaying and Summarizing Data 31 3.1 Graphically Displaying a Single Variable 32 3.2 Graphically Comparing Two Samples 39 3.3 Measures of Location 41 3.4 Measures of Spread 44 3.5 Displaying Relationships Between Two or More Variables 46 3.6 Measures of Association for Two or More Variables 49 Exercises 52 4 Logic, Probability, and Uncertainty 59 4.1 Deductive Logic and Plausible Reasoning 60 4.2 Probability 62 4.3 Axioms of Probability 64 4.4 Joint Probability and Independent Events 65 4.5 Conditional Probability 66 4.6 Bayes' Theorem 68 4.7 Assigning Probabilities 74 4.8 Odds and Bayes Factor 75 4.9 Beat the Dealer 76 Exercises 80 5 Discrete Random Variables 83 5.1 Discrete Random Variables 84 5.2 Probability Distribution of a Discrete Random Variable 86 5.3 Binomial Distribution 90 5.4 Hypergeometric Distribution 92 5.5 Poisson Distribution 93 5.6 Joint Random Variables 96 5.7 Conditional Probability for Joint Random Variables 100 Exercises 104 6 Bayesian Inference for Discrete Random Variables 109 6.1 Two Equivalent Ways of Using Bayes' Theorem 114 6.2 Bayes' Theorem for Binomial with Discrete Prior 116 6.3 Important Consequences of Bayes' Theorem 119 6.4 Bayes' Theorem for Poisson with Discrete Prior 120 Exercises 122 Computer Exercises 126 7 Continuous Random Variables 129 7.1 Probability Density Function 131 7.2 Some Continuous Distributions 135 7.3 Joint Continuous Random Variables 143 7.4 Joint Continuous and Discrete Random Variables 144 Exercises 147 8 Bayesian Inference for Binomial Proportion 149 8.1 Using a Uniform Prior 150 8.2 Using a Beta Prior 151 8.3 Choosing Your Prior 154 8.4 Summarizing the Posterior Distribution 158 8.5 Estimating the Proportion 161 8.6 Bayesian Credible Interval 162 Exercises 164 Computer Exercises 167 9 Comparing Bayesian and Frequentist Inferences for Proportion 169 9.1 Frequentist Interpretation of Probability and Parameters 170 9.2 Point Estimation 171 9.3 Comparing Estimators for Proportion 174 9.4 Interval Estimation 175 9.5 Hypothesis Testing 178 9.6 Testing a OneSided Hypothesis 179 9.7 Testing a TwoSided Hypothesis 182 Exercises 187 Monte Carlo Exercises 190 10 Bayesian Inference for Poisson 193 10.1 Some Prior Distributions for Poisson 194 10.2 Inference for Poisson Parameter 200 Exercises 207 Computer Exercises 208 11 Bayesian Inference for Normal Mean 211 11.1 Bayes' Theorem for Normal Mean with a Discrete Prior 211 11.2 Bayes' Theorem for Normal Mean with a Continuous Prior 218 11.3 Choosing Your Normal Prior 222 11.4 Bayesian Credible Interval for Normal Mean 224 11.5 Predictive Density for Next Observation 227 Exercises 230 Computer Exercises 232 12 Comparing Bayesian and Frequentist Inferences for Mean 237 12.1 Comparing Frequentist and Bayesian Point Estimators 238 12.2 Comparing Condence and Credible Intervals for Mean 241 12.3 Testing a OneSided Hypothesis about a Normal Mean 243 12.4 Testing a TwoSided Hypothesis about a Normal Mean 247 Exercises 251 13 Bayesian Inference for Di erence Between Means 255 13.1 Independent Random Samples from Two Normal Distributions 256 13.2 Case
 1: Equal Variances 257 13.3 Case
 2: Unequal Variances 262 13.4 Bayesian Inference for Dierence Between Two Proportions Using Normal Approximation 265 13.5 Normal Random Samples from Paired Experiments 266 Exercises 272 14 Bayesian Inference for Simple Linear Regression 283 14.1 Least Squares Regression 284 14.2 Exponential Growth Model 288 14.3 Simple Linear Regression Assumptions 290 14.4 Bayes' Theorem for the Regression Model 292 14.5 Predictive Distribution for Future Observation 298 Exercises 303 Computer Exercises 312 15 Bayesian Inference for Standard Deviation 315 15.1 Bayes' Theorem for Normal Variance with a Continuous Prior 316 15.2 Some Specic Prior Distributions and the Resulting Posteriors 318 15.3 Bayesian Inference for Normal Standard Deviation 326 Exercises 332 Computer Exercises 335 16 Robust Bayesian Methods 337 16.1 Eect of Misspecied Prior 338 16.2 Bayes' Theorem with Mixture Priors 340 Exercises 349 Computer Exercises 351 17 Bayesian Inference for Normal with Unknown Mean and Variance 355 17.1 The Joint Likelihood Function 358 17.2 Finding the Posterior when Independent Jeffreys' Priors for and 2 Are Used 359 17.3 Finding the Posterior when a Joint Conjugate Prior for and 2 Is Used 361 17.4 Difference Between Normal Means with Equal Unknown Variance 367 17.5 Difference Between Normal Means with Unequal Unknown Variances 377 Computer Exercises 383 Appendix: Proof that the Exact Marginal Posterior Distribution of is Student's t 385 18 Bayesian Inference for Multivariate Normal Mean Vector 393 18.1 Bivariate Normal Density 394 18.2 Multivariate Normal Distribution 397 18.3 The Posterior Distribution of the Multivariate Normal Mean Vector when Covariance Matrix Is Known 398 18.4 Credible Region for Multivariate Normal Mean Vector when Covariance Matrix Is Known 400 18.5 Multivariate Normal Distribution with Unknown Covariance Matrix 402 Computer Exercises 406 19 Bayesian Inference for the Multiple Linear Regression Model 411 19.1 Least Squares Regression for Multiple Linear Regression Model 412 19.2 Assumptions of Normal Multiple Linear Regression Model 414 19.3 Bayes' Theorem for Normal Multiple Linear Regression Model 415 19.4 Inference in the Multivariate Normal Linear Regression Model 419 19.5 The Predictive Distribution for a Future Observation 425 Computer Exercises 428 20 Computational Bayesian Statistics Including Markov Chain Monte Carlo 431 20.1 Direct Methods for Sampling from the Posterior 436 20.2 Sampling  Importance  Resampling 450 20.3 Markov Chain Monte Carlo Methods 454 20.4 Slice Sampling 470 20.5 Inference from a Posterior Random Sample 473 20.6 Where to Next? 475 A Introduction to Calculus 477 B Use of Statistical Tables 497 C Using the Included Minitab Macros 523 D Using the Included R Functions 543 E Answers to Selected Exercises 565 References 591 Index 595.
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Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

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QA279.5 .B65 2017  Unknown 
12. Bayesian inference for partially identified models : exploring the limits of limited data [2015]
 Gustafson, Paul, 1968 author.
 Boca Raton, Florida : CRC Press, Taylor & Francis Group, 2015.
 Description
 Book — xxi, 174 pages : ill. ; 24 cm.
 Summary

 Introduction Identification What Is against Us? What Is for Us? Some Simple Examples of Partially Identified Models The Road Ahead
 The Structure of Inference in Partially Identified Models Bayesian Inference The Structure of Posterior Distributions in PIMs Computational Strategies Strength of Bayesian Updating, Revisited Posterior Moments Credible Intervals Evaluating the Worth of Inference
 Partial Identification versus Model Misspecification The Siren Call of Identification Comparing Bias Reflecting Uncertainty A Further Example Other Investigations of PIM versus IPMM
 Models Involving Misclassification Binary to Trinary Misclassification Binary Misclassification across Three Populations
 Models Involving Instrumental Variables What Is an Instrumental Variable? Imperfect Compliance Modeling an Approximate Instrumental Variable
 Further Examples Inference in the Face of a Hidden Subpopulation Ecological Inference, Revisited
 Further Topics Computational Considerations Study Design Considerations Applications
 Concluding Thoughts What Have Others Said? What Is the Road ahead?
 Index.
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Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA279.5 .G87 2015  Unknown 
 Boca Raton, FL : CRC Press, c2015.
 Description
 Book — xxxix, 640 p. : ill., maps ; 24 cm
 Summary

 Bayesian Inference on the Brain John A.D. Aston and Adam M. Johansen Forecasting Indian Macroeconomic Variables Using MediumScale VAR Models Goodness C. Aye, Pami Dua, and Rangan Gupta Comparing Proportions: A Modern Solution to a Classical Problem Jose M. Bernardo Hamiltonian Monte Carlo for Hierarchical Models Michael Betancourt and Mark Girolami On Bayesian SpatioTemporal Modeling of Oceanographic Climate Characteristics Madhuchhanda Bhattacharjee and Snigdhansu Chatterjee Sequential Bayesian Inference for Dynamic State Space Model Parameters Arnab Bhattacharya and Simon Wilson Bayesian Active Contours with AffineInvariant Elastic Shape Prior Darshan Bryner and Anuj Srivastava Bayesian Semiparametric Longitudinal Data Modeling Using NI Densities Luis M. Castro, Victor H. Lachos, Diana M. Galvis, and Dipankar Bandyopadhyay Bayesian Factor Analysis Based on Concentration Yun Cao, Michael Evans, and Irwin Guttman Regional Fertility Data Analysis: A Small Area Bayesian Approach Eduardo A. Castro, Zhen Zhang, Arnab Bhattacharjee, Jose M. Martins, and Tapabrata Maiti In Search of Optimal Objective Priors for Model Selection and Estimation Jyotishka Datta and Jayanta K. Ghosh Bayesian Variable Selection for Predictively Optimal Regression Tanujit Dey and Ernest Fokoue Scalable Subspace Clustering with Application to Motion Segmentation Liangjing Ding and Adrian Barbu Bayesian Inference for Logistic Regression Models Using Sequential Posterior Simulation John Geweke, Garland Durham, and Huaxin Xu From Risk Analysis to Adversarial Risk Analysis David Rios Insua, Javier Cano, Michael Pellot, and Ricardo Ortega Symmetric Power Link with Ordinal Response Model Xun Jiang and Dipak K. Dey Elastic Prior Shape Models of 3D Objects for Bayesian Image Analysis Sebastian Kurtek and Qian Xie MultiState Models for Disease Natural History Amy E. Laird, Rebecca A. Hubbard, and Lurdes Y.T. Inoue Priors on Hypergraphical Models via Simplicial Complexes Simon Lunagomez, Sayan Mukherjee, and Robert Wolpert A Bayesian Uncertainty Analysis for Nonignorable Nonresponse Balgobin Nandram and Namkyo Woo Stochastic Volatility and Realized Stochastic Volatility Models Yasuhiro Omori and Toshiaki Watanabe Monte Carlo Methods and Zero Variance Principle Theodore Papamarkou, Antonietta Mira, and Mark Girolami A Flexible Class of Reduced Rank Spatial Models for Large NonGaussian Dataset Rajib Paul, Casey M. Jelsema, and Kwok Wai Lau A Bayesian Reweighting Technique for Small Area Estimation Azizur Rahman and Satyanshu K. Upadhyay Empirical Bayes Methods for the Transformed Gaussian Random Field Model with Additive Measurement Errors Vivekananda Roy, Evangelos Evangelou, and Zhengyuan Zhu Mixture Kalman Filters and Beyond Saikat Saha, Gustaf Hendeby, and Fredrik Gustafsson Some Aspects of Bayesian Inference in Skewed Mixed Logistic Regression Models Cristiano C. Santos and Rosangela H. Loschi A Bayesian Analysis of the Solar Cycle Using Multiple Proxy Variables David C. Stenning, David A. van Dyk, Yaming Yu, and Vinay Kashyap Fuzzy Information, Likelihood, Bayes' Theorem, and Engineering Application Reinhard Viertl and Owat Sunanta Bayesian Parallel Computation for Intractable Likelihood Using GriddyGibbs Sampler Nuttanan Wichitaksorn and S.T. Boris Choy Index.
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Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA279.5 .C87 2015  Unknown 
14. Bayesian data analysis [2014]
 Gelman, Andrew, author.
 Third edition  Boca Raton : CRC Press, [2014]
 Description
 Book — xiv, 667 pages : illustrations ; 27 cm
 Summary

 FUNDAMENTALS OF BAYESIAN INFERENCE Probability and Inference SingleParameter Models Introduction to Multiparameter Models Asymptotics and Connections to NonBayesian Approaches Hierarchical Models
 FUNDAMENTALS OF BAYESIAN DATA ANALYSIS Model Checking Evaluating, Comparing, and Expanding Models Modeling Accounting for Data Collection Decision Analysis
 ADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional Approximations
 REGRESSION MODELS Introduction to Regression Models Hierarchical Linear Models Generalized Linear Models Models for Robust Inference Models for Missing Data
 NONLINEAR AND NONPARAMETRIC MODELS Parametric Nonlinear Models Basic Function Models Gaussian Process Models Finite Mixture Models Dirichlet Process Models
 APPENDICES A: Standard Probability Distributions B: Outline of Proofs of Asymptotic Theorems C: Computation in R and Stan
 Bibliographic Notes and Exercises appear at the end of each chapter.
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 Online
Law Library (Crown)
Law Library (Crown)  Status 

Find it Basement  Request (opens in new tab) 
QA279.5 .G45 2014  Unavailable Missing Request 
15. Bayesian data analysis [2014]
 Gelman, Andrew, author.
 Third edition.  Boca Raton : CRC Press, Taylor & Francis Group, [2014]
 Description
 Book — xiv, 661 pages : illustrations ; 27 cm.
 Summary

 FUNDAMENTALS OF BAYESIAN INFERENCE Probability and Inference SingleParameter Models Introduction to Multiparameter Models Asymptotics and Connections to NonBayesian Approaches Hierarchical Models
 FUNDAMENTALS OF BAYESIAN DATA ANALYSIS Model Checking Evaluating, Comparing, and Expanding Models Modeling Accounting for Data Collection Decision Analysis
 ADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional Approximations
 REGRESSION MODELS Introduction to Regression Models Hierarchical Linear Models Generalized Linear Models Models for Robust Inference Models for Missing Data
 NONLINEAR AND NONPARAMETRIC MODELS Parametric Nonlinear Models Basic Function Models Gaussian Process Models Finite Mixture Models Dirichlet Process Models
 APPENDICES A: Standard Probability Distributions B: Outline of Proofs of Asymptotic Theorems C: Computation in R and Stan
 Bibliographic Notes and Exercises appear at the end of each chapter.
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Marine Biology Library (Miller), Science Library (Li and Ma)
Marine Biology Library (Miller)  Status 

Stacks  
QA279.5 .B386 2014  Unknown 
Science Library (Li and Ma)  Status 

Stacks  
QA279.5 .B386 2014  Unknown 
 Cowles, Mary Kathryn.
 New York : Springer, ©2013.
 Description
 Book — 1 online resource (xiv, 232 pages) Digital: text file.PDF.
 Summary

 What is Bayesian statistics?
 Review of probability
 Introduction to oneparameter models : estimating a population proportion
 Inference for a population proportion
 Special considerations in Bayesian inference
 Other oneparameter models and their conjugate priors
 More realism please : introduction to multiparameter models
 Fitting more complex Bayesian models : Markov chain Monte Carlo
 Hierarchical models and more on convergence assessment
 Regression on hierarchical regression models
 Model comparison, model checking, and hypothesis testing.
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 Stone, James V., author.
 Lexington, Kentucky: Sebtel Press, 2013.
 Description
 Book — 170 pages : ill. ; 23 cm
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA279.5 .S766 2013  Unknown CHECKEDOUT 
 Oxford : Oxford University Press, 2013.
 Description
 Book — 1 online resource (xiii, 702 p.) : ill.
 Summary

 I EXCHANGEABILITY
 II HIERARCHICAL MODELS
 III MARKOV CHAIN MONTE CARLO
 IV DYNAMIC MODELS
 V SEQUENTIAL MONTE CARLO
 VI NONPARAMETRICS
 VII SPLINE MODELS AND COPULAS
 VIII MODEL ELABORATION AND PRIOR DISTRIBUTIONS
 IX REGRESSIONS AND MODEL AVERAGING
 X FINANCE AND ACTUARIAL SCIENCE
 XI MEDICINE AND BIOSTATISTICS
 XII INVERSE PROBLEMS AND APPLICATIONS.
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19. Bayesian statistics : an introduction [2012]
 Lee, Peter M.
 4th ed.  Chichester, West Sussex ; Hoboken, N.J. : Wiley, 2012.
 Description
 Book — xxiii, 462 p. : ill. ; 23 cm.
 Summary

 Preface xix Preface to the First Edition xxi 1 Preliminaries 1 1.1 Probability and Bayes' Theorem 1 1.2 Examples on Bayes' Theorem 9 1.3 Random variables 12 1.4 Several random variables 17 1.5 Means and variances 23 1.6 Exercises on Chapter 1 31 2 Bayesian inference for the normal distribution 36 2.1 Nature of Bayesian inference 36 2.2 Normal prior and likelihood 40 2.3 Several normal observations with a normal prior 44 2.4 Dominant likelihoods 48 2.5 Locally uniform priors 50 2.6 Highest density regions 54 2.7 Normal variance 55 2.8 HDRs for the normal variance 59 2.9 The role of sufficiency 60 2.10 Conjugate prior distributions 67 2.11 The exponential family 71 2.12 Normal mean and variance both unknown 73 2.13 Conjugate joint prior for the normal distribution 78 2.14 Exercises on Chapter 2 82 3 Some other common distributions 85 3.1 The binomial distribution 85 3.2 Reference prior for the binomial likelihood 92 3.3 Jeffreys' rule 96 3.4 The Poisson distribution 102 3.5 The uniform distribution 106 3.6 Reference prior for the uniform distribution 110 3.6.1 Lower limit of the interval fixed 110 3.7 The tramcar problem 113 3.8 The first digit problem invariant priors 114 3.9 The circular normal distribution 117 3.10 Approximations based on the likelihood 122 3.11 Reference posterior distributions 128 3.12 Exercises on Chapter 3 134 4 Hypothesis testing 138 4.1 Hypothesis testing 138 4.2 Onesided hypothesis tests 143 4.3 Lindley's method 145 4.4 Point (or sharp) null hypotheses with prior information 146 4.5 Point null hypotheses for the normal distribution 150 4.6 The Doogian philosophy 157 4.7 Exercises on Chapter 4 158 5 Twosample problems 162 5.1 Twosample problems
 both variances unknown 162 5.2 Variances unknown but equal 165 5.3 Variances unknown and unequal (BehrensFisher problem) 168 5.4 The BehrensFisher controversy 171 5.5 Inferences concerning a variance ratio 173 5.6 Comparison of two proportions the 2 × 2 table 176 5.7 Exercises on Chapter 5 179 6 Correlation, regression and the analysis of variance 182 6.1 Theory of the correlation coefficient 182 6.2 Examples on the use of the correlation coefficient 189 6.3 Regression and the bivariate normal model 190 6.4 Conjugate prior for the bivariate regression model 197 6.5 Comparison of several means
 the one way model 200 6.6 The two way layout 209 6.7 The general linear model 212 6.8 Exercises on Chapter 6 217 7 Other topics 221 7.1 The likelihood principle 221 7.2 The stopping rule principle 226 7.3 Informative stopping rules 229 7.4 The likelihood principle and reference priors 232 7.5 Bayesian decision theory 234 7.6 Bayes linear methods 240 7.7 Decision theory and hypothesis testing 243 7.8 Empirical Bayes methods 245 7.9 Exercises on Chapter 7 247 8 Hierarchical models 253 8.1 The idea of a hierarchical model 253 8.2 The hierarchical normal model 258 8.3 The baseball example 262 8.4 The Stein estimator 264 8.5 Bayesian analysis for an unknown overall mean 268 8.6 The general linear model revisited 272 8.7 Exercises on Chapter 8 277 9 The Gibbs sampler and other numerical methods 281 9.1 Introduction to numerical methods 281 9.2 The EM algorithm 283 9.3 Data augmentation by Monte Carlo 291 9.4 The Gibbs sampler 294 9.5 Rejection sampling 311 9.6 The MetropolisHastings algorithm 317 9.7 Introduction to WinBUGS and OpenBUGS 323 9.8 Generalized linear models 332 9.9 Exercises on Chapter 9 335 10 Some approximate methods 340 10.1 Bayesian importance sampling 340 10.2 Variational Bayesian methods: simple case 345 10.3 Variational Bayesian methods: general case 353 10.4 ABC : Approximate Bayesian Computation 356 10.5 Reversible jump Markov chain Monte Carlo 367 10.6 Exercises on Chapter 10 369 Appendix A Common statistical distributions 373 A.1 Normal distribution 374 A.2 Chisquared distribution 375 A.3 Normal approximation to chisquared 376 A.4 Gamma distribution 376 A.5 Inverse chisquared distribution 377 A.6 Inverse chi distribution 378 A.7 Log chisquared distribution 379 A.8 Student's t distribution 380 A.9 Normal/chisquared distribution 381 A.10 Beta distribution 382 A.11 Binomial distribution 383 A.12 Poisson distribution 384 A.13 Negative binomial distribution 385 A.14 Hypergeometric distribution 386 A.15 Uniform distribution 387 A.16 Pareto distribution 388 A.17 Circular normal distribution 389 A.18 Behrens' distribution 391 A.19 Snedecor's F distribution 393 A.20 Fisher's z distribution 393 A.21 Cauchy distribution 394 A.22 The probability that one beta variable is greater than another 395 A.23 Bivariate normal distribution 395 A.24 Multivariate normal distribution 396 A.25 Distribution of the correlation coefficient 397 Appendix B Tables 399 B.1 Percentage points of the BehrensFisher distribution 399 B.2 Highest density regions for the chisquared distribution 402 B.3 HDRs for the inverse chisquared distribution 404 B.4 Chisquared corresponding to HDRs for log chisquared 406 B.5 Values of F corresponding to HDRs for log F 408 Appendix C R programs 430 Appendix D Further reading 436 D.1 Robustness 436 D.2 Nonparametric methods 436 D.3 Multivariate estimation 436 D.4 Time series and forecasting 437 D.5 Sequential methods 437 D.6 Numerical methods 437 D.7 Bayesian networks 437 D.8 General reading 438 References 439 Index 455.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA279.5 .L44 2012  Unknown 
 Boca Raton, FL : CRC Press, c2011.
 Description
 Book — xvii, 498 p. : ill. ; 27 cm.
 Summary

 Prologue Probability of a Defective: Binomial Data Brass Alloy Zinc Content: Normal Data Armadillo Hunting: Poisson Data Abortion in Dairy Cattle: Survival Data Ache Hunting with Age Trends Lung Cancer Treatment: LogNormal Regression Survival with Random Effects: Ache Hunting Fundamental Ideas I Simple Probability Computations Science, Priors, and Prediction Statistical Models Posterior Analysis Commonly Used Distributions Integration versus Simulation Introduction WinBUGS I: Getting Started Method of Composition Monte Carlo Integration Posterior Computations in R Fundamental Ideas II Statistical Testing Exchangeability Likelihood Functions Sufficient Statistics Analysis Using Predictive Distributions Flat Priors Jeffreys' Priors Bayes Factors Other Model Selection Criteria Normal Approximations to Posteriors Bayesian Consistency and Inconsistency Hierarchical Models Some Final Comments on Likelihoods Identifiability and Noninformative Data Comparing Populations Inference for Proportions Inference for Normal Populations Inference for Rates Sample Size Determination Illustrations: Foundry Data Medfly Data Radiological Contrast Data Reyes Syndrome Data Corrosion Data Diasorin Data Ache Hunting Data Breast Cancer Data Simulations Generating Random Samples Traditional Monte Carlo Methods Basics of Markov Chain Theory Markov Chain Monte Carlo Basic Concepts of Regression Introduction Data Notation and Format Predictive Models: An Overview Modeling with Linear Structures Illustration: FEV Data Binomial Regression The Sampling Model Binomial Regression Analysis Model Checking Prior Distributions Mixed Models Illustrations: Space Shuttle Data Trauma Data Onychomycosis Fungis Data Cow Abortion Data Linear Regression The Sampling Model Reference Priors Conjugate Priors Independence Priors ANOVA Model Diagnostics Model Selection Nonlinear Regression Illustrations: FEV Data Bank Salary Data Diasorin Data Coleman Report Data Dugong Growth Data Correlated Data Introduction Mixed Models Multivariate Normal Models Multivariate Normal Regression Posterior Sampling and Missing Data Illustrations: Interleukin Data Sleeping Dog Data MetaAnalysis Data Dental Data Count Data Poisson Regression OverDispersion and Mixtures of Poissons Longitudinal Data Illustrations: Ache Hunting Data Textile Faults Data Coronary Heart Disease Data Foot and Mouth Disease Data Time to Event Data Introduction OneSample Models TwoSample Data Plotting Survival and Hazard Functions Illustrations: Leukemia Cancer Data Breast Cancer Data Time to Event Regression Accelerated Failure Time Models Proportional Hazards Modeling Survival with Random Effects Illustrations: Leukemia Cancer Data Larynx Cancer Data Cow Abortion Data Kidney Transplant Data Lung Cancer Data Ache Hunting Data Binary Diagnostic Tests Basic Ideas One Test, One Population Two Tests, Two Populations Prevalence Distributions Illustrations: Coronary Artery Disease Paratuberculosis Data Nucleospora Salmonis Data Ovine Progressive Pnemonia Data Nonparametric Models Flexible Density Shapes Flexible Regression Functions Proportional Hazards Modeling Illustrations: Galaxy Data ELISA Data for Johnes Disease Fungus Data Test Engine Data Lung Cancer Data Appendix A: Matrices and Vectors Appendix B: Probability Appendix C: Getting Started in R References.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
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Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

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QA279.5 .B3868 2011  Unknown 
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