05396nam 22006854a 450 991014504500332120170809153817.01-280-28770-597866102877030-470-09239-40-470-09238-6(CKB)1000000000239293(EBL)242953(OCoLC)475962262(SSID)ssj0000109309(PQKBManifestationID)11129594(PQKBTitleCode)TC0000109309(PQKBWorkID)10045545(PQKB)10889214(MiAaPQ)EBC242953(EXLCZ)99100000000023929320050222d2005 uy 0engur|n|---|||||txtccrBayesian models for categorical data[electronic resource] /Peter CongdonChichester ;New York Wileyc20051 online resource (448 p.)Wiley series in probability and statisticsDescription based upon print version of record.0-470-09237-8 Includes bibliographical references and index.Bayesian Models for Categorical Data; Contents; Preface; Chapter 1 Principles of Bayesian Inference; 1.1 Bayesian updating; 1.2 MCMC techniques; 1.3 The basis for MCMC; 1.4 MCMC sampling algorithms; 1.5 MCMC convergence; 1.6 Competing models; 1.7 Setting priors; 1.8 The normal linear model and generalized linear models; 1.9 Data augmentation; 1.10 Identifiability; 1.11 Robustness and sensitivity; 1.12 Chapter themes; References; Chapter 2 Model Comparison and Choice; 2.1 Introduction: formal methods, predictive methods and penalized deviance criteria; 2.2 Formal Bayes model choice2.3 Marginal likelihood and Bayes factor approximations2.4 Predictive model choice and checking; 2.5 Posterior predictive checks; 2.6 Out-of-sample cross-validation; 2.7 Penalized deviances from a Bayes perspective; 2.8 Multimodel perspectives via parallel sampling; 2.9 Model probability estimates from parallel sampling; 2.10 Worked example; References; Chapter 3 Regression for Metric Outcomes; 3.1 Introduction: priors for the linear regression model; 3.2 Regression model choice and averaging based on predictor selection; 3.3 Robust regression methods: models for outliers3.4 Robust regression methods: models for skewness and heteroscedasticity3.5 Robustness via discrete mixture models; 3.5.1 Complete data representation; 3.5.2 Identification issues; 3.5.3 Dirichlet process mixture models; 3.6 Non-linear regression effects via splines and other basis functions; 3.6.1 Penalized random effects for spline coefficients; 3.6.2 Basis function regression; 3.6.3 Special spline functions; 3.7 Dynamic linear models and their application in non-parametric regression; 3.7.1 Some common forms of DLM; 3.7.2 Robust errors; 3.7.3 General additive models3.7.4 Alternative smoothness priorsExercises; References; Chapter 4 Models for Binary and Count Outcomes; 4.1 Introduction: discrete model likelihoods vs. data augmentation; 4.1.1 Count data; 4.1.2 Binomial and binary data; 4.2 Estimation by data augmentation: the Albert-Chib method; 4.2.1 Other augmented data methods; 4.3 Model assessment: outlier detection and model checks; 4.3.1 Model assessment: predictive model selection and checks; 4.4 Predictor selection in binary and count regression; 4.5 Contingency tables4.6 Semi-parametric and general additive models for binomial and count responses4.6.1 Robust and adaptive non-parametric regression; 4.6.2 Other approaches to non-linearity; Exercises; References; Chapter 5 Further Questions in Binomial and Count Regression; 5.1 Generalizing the Poisson and binomial: overdispersion and robustness; 5.2 Continuous mixture models; 5.2.1 Modified exponential families; 5.3 Discrete mixtures; 5.4 Hurdle and zero-inflated models; 5.5 Modelling the link function; 5.5.1 Discrete (DPP mixture); 5.5.2 Parametric link transformations5.5.3 Beta mixture on cumulative densitiesThe use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to students and researchers alike. Emphasizing the use of statistical computing and applied data analysis, this book provides a comprehensive introduction to Bayesian methods of categorical outcomes.* Reviews recent Bayesian methodology for categorical outcomes (binary, count and multinomial data).* Considers missing daWiley series in probability and statistics.Bayesian statistical decision theoryMonte Carlo methodMarkov processesMultivariate analysisElectronic books.Bayesian statistical decision theory.Monte Carlo method.Markov processes.Multivariate analysis.519.542Congdon P145037MiAaPQMiAaPQMiAaPQBOOK9910145045003321Bayesian models for categorical data737826UNINA01910nam 2200529Ia 450 991070183170332120120710113523.0(CKB)5470000002421793(OCoLC)798669746(EXLCZ)99547000000242179320120710d2012 ua 0engurcn|||||||||txtrdacontentcrdamediacrrdacarrierModel of the radio frequency (RF) excitation response from monopole and dipole antennas in a large scale tank[electronic resource] /Jeffrey D. Wilson and Gregory A. ZimmerliCleveland, Ohio :National Aeronautics and Space Administration, Glenn Research Center,[2012]1 online resource (17 pages) color illustrationsNASA/TM ;2012-217267Title from title screen (viewed on July 10, 2012)."January 2012."Includes bibliographical references (page 17).Model of the radio frequency Antenna designnasatComputerized simulationnasatCoupled modesnasatExcitationnasatMonopole antennasnasatRadio frequenciesnasatPropellant tanksnasatAntenna design.Computerized simulation.Coupled modes.Excitation.Monopole antennas.Radio frequencies.Propellant tanks.Wilson Jeffrey D1398398Zimmerli Gregory A1398399NASA Glenn Research Center.GPOGPOBOOK9910701831703321Model of the radio frequency (RF) excitation response from monopole and dipole antennas in a large scale tank3461569UNINA