The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics.
The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics.
Paul Damien is a Professor at the McCombs School of Business, University of Texas in Austin. Petros Dellaportas is a Professor at the Athens University of Economics and Business. Nicholas G Polson is Professor of Econometrics and Statistics at Chicago Booth, University of Chicago. David M Stephens is a Professor in the Department of Mathematics and Statistics at McGill University, Canada.
Inhaltsangabe
* Introduction * I EXCHANGEABILITY * 1: Michael Goldstein: Observables and Models: exchangeability and the inductive argument * 2: A. Philip Dawid: Exchangeability and its Ramifications * II HIERARCHICAL MODELS * 3: Alan E. Gelfand and Souparno Ghosh: Hierarchical Modeling * 4: Sounak Chakraborty, Bani K Mallick and Malay Ghosh: Bayesian Hierarchical Kernel Machines for Nonlinear Regression and Classification * 5: Athanasios Kottas and Kassandra Fronczyk: Flexible Bayesian modelling for clustered categorical responses in developmental toxicology * III MARKOV CHAIN MONTE CARLO * 6: Siddartha Chib: Markov chain Monte Carlo Methods * 7: Jim E. Griffin and David A. Stephens: Advances in Markov chain Monte Carlo * IV DYNAMIC MODELS * 8: Mike West: Bayesian Dynamic Modelling * 9: Dani Gamerman and Esther Salazar: Hierarchical modeling in time series: the factor analytic approach * 10: Gabriel Huerta and Glenn A. Stark: Dynamic and spatial modeling of block maxima extremes * V SEQUENTIAL MONTE CARLO * 11: Hedibert F. Lopes and Carlos M. Carvalho: Online Bayesian learning in dynamic models: An illustrative introduction to particle methods * 12: Ana Paula Sales, Christopher Challis, Ryan Prenger, and Daniel Merl: Semi-supervised Classification of Texts Using Particle Learning for Probabilistic Automata * VI NONPARAMETRICS * 13: Stephen G Walker: Bayesian Nonparametrics * 14: Ramsés H. Mena: Geometric Weight Priors and their Applications * 15: Stephen G. Walker and George Karabatsos: Revisiting Bayesian Curve Fitting Using Multivariate Normal Mixtures * VII SPLINE MODELS AND COPULAS * 16: Sally Wood: Applications of Bayesian Smoothing Splines * 17: Michael Stanley Smith: Bayesian Approaches to Copula Modelling * VIII MODEL ELABORATION AND PRIOR DISTRIBUTIONS * 18: M.J. Bayarri and J.O. Berger: Hypothesis Testing and Model Uncertainty * 19: E. Gutiérrez-Peña and M. Mendoza: Proper and non-informative conjugate priors for exponential family models * 20: David Draper: Bayesian Model Specification: Heuristics and Examples * 21: Zesong Liu, Jesse Windle, and James G. Scott: Case studies in Bayesian screening for time-varying model structure: The partition problem * IX REGRESSIONS AND MODEL AVERAGING * 22: Hugh A. Chipman, Edward I. George and Robert E. McCulloch: Bayesian Regression Structure Discovery * 23: Robert B. Gramacy: Gibbs sampling for ordinary, robust and logistic regression with Laplace priors * 24: Merlise Clyde and Edwin S. Iversen: Bayesian Model Averaging in the M-Open Framework * X FINANCE AND ACTUARIAL SCIENCE * 25: Eric Jacquier and Nicholas G Polson: Asset Allocation in Finance: A Bayesian Perspective * 26: Arthur Korteweg: Markov Chain Monte Carlo Methods in Corporate Finance * 27: Udi Makov: Actuarial Credibity Theory and Bayesian Statistics - The Story of a Special Evolution * XI MEDICINE AND BIOSTATISTICS * 28: Peter Müller: Bayesian Models in Biostatistics and Medicine * 29: Purushottam W. Laud, Siva Sivaganesan and Peter Müller: Subgroup Analysis * 30: Timothy E. Hanson and Alejandro Jara: Surviving Fully Bayesian Nonparametric Regression Models * XII INVERSE PROBLEMS AND APPLICATIONS * 31: Colin Fox, Heikki Haario and J. Andrés Christen: Inverse Problems * 32: Jari Kaipio and Ville Kolehmainen: Approximate marginalization over modeling errors and uncertainties in inverse problems * 33: C. Nakhleh, D. Higdon, C. K. Allen and R. Ryne: Bayesian reconstruction of particle beam phase space
* Introduction * I EXCHANGEABILITY * 1: Michael Goldstein: Observables and Models: exchangeability and the inductive argument * 2: A. Philip Dawid: Exchangeability and its Ramifications * II HIERARCHICAL MODELS * 3: Alan E. Gelfand and Souparno Ghosh: Hierarchical Modeling * 4: Sounak Chakraborty, Bani K Mallick and Malay Ghosh: Bayesian Hierarchical Kernel Machines for Nonlinear Regression and Classification * 5: Athanasios Kottas and Kassandra Fronczyk: Flexible Bayesian modelling for clustered categorical responses in developmental toxicology * III MARKOV CHAIN MONTE CARLO * 6: Siddartha Chib: Markov chain Monte Carlo Methods * 7: Jim E. Griffin and David A. Stephens: Advances in Markov chain Monte Carlo * IV DYNAMIC MODELS * 8: Mike West: Bayesian Dynamic Modelling * 9: Dani Gamerman and Esther Salazar: Hierarchical modeling in time series: the factor analytic approach * 10: Gabriel Huerta and Glenn A. Stark: Dynamic and spatial modeling of block maxima extremes * V SEQUENTIAL MONTE CARLO * 11: Hedibert F. Lopes and Carlos M. Carvalho: Online Bayesian learning in dynamic models: An illustrative introduction to particle methods * 12: Ana Paula Sales, Christopher Challis, Ryan Prenger, and Daniel Merl: Semi-supervised Classification of Texts Using Particle Learning for Probabilistic Automata * VI NONPARAMETRICS * 13: Stephen G Walker: Bayesian Nonparametrics * 14: Ramsés H. Mena: Geometric Weight Priors and their Applications * 15: Stephen G. Walker and George Karabatsos: Revisiting Bayesian Curve Fitting Using Multivariate Normal Mixtures * VII SPLINE MODELS AND COPULAS * 16: Sally Wood: Applications of Bayesian Smoothing Splines * 17: Michael Stanley Smith: Bayesian Approaches to Copula Modelling * VIII MODEL ELABORATION AND PRIOR DISTRIBUTIONS * 18: M.J. Bayarri and J.O. Berger: Hypothesis Testing and Model Uncertainty * 19: E. Gutiérrez-Peña and M. Mendoza: Proper and non-informative conjugate priors for exponential family models * 20: David Draper: Bayesian Model Specification: Heuristics and Examples * 21: Zesong Liu, Jesse Windle, and James G. Scott: Case studies in Bayesian screening for time-varying model structure: The partition problem * IX REGRESSIONS AND MODEL AVERAGING * 22: Hugh A. Chipman, Edward I. George and Robert E. McCulloch: Bayesian Regression Structure Discovery * 23: Robert B. Gramacy: Gibbs sampling for ordinary, robust and logistic regression with Laplace priors * 24: Merlise Clyde and Edwin S. Iversen: Bayesian Model Averaging in the M-Open Framework * X FINANCE AND ACTUARIAL SCIENCE * 25: Eric Jacquier and Nicholas G Polson: Asset Allocation in Finance: A Bayesian Perspective * 26: Arthur Korteweg: Markov Chain Monte Carlo Methods in Corporate Finance * 27: Udi Makov: Actuarial Credibity Theory and Bayesian Statistics - The Story of a Special Evolution * XI MEDICINE AND BIOSTATISTICS * 28: Peter Müller: Bayesian Models in Biostatistics and Medicine * 29: Purushottam W. Laud, Siva Sivaganesan and Peter Müller: Subgroup Analysis * 30: Timothy E. Hanson and Alejandro Jara: Surviving Fully Bayesian Nonparametric Regression Models * XII INVERSE PROBLEMS AND APPLICATIONS * 31: Colin Fox, Heikki Haario and J. Andrés Christen: Inverse Problems * 32: Jari Kaipio and Ville Kolehmainen: Approximate marginalization over modeling errors and uncertainties in inverse problems * 33: C. Nakhleh, D. Higdon, C. K. Allen and R. Ryne: Bayesian reconstruction of particle beam phase space
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