The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Matt P. Wand - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Analysis for penalized spline regression using winbugs
Journal of Statistical Software, 2005Co-Authors: Ciprian M. Crainiceanu, David Ruppert, Matt P. WandAbstract:Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian Analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian Analysis in WinBUGS. Good mixing properties of the MCMC chains are obtained by using low-rank thin-plate splines, while simulation times per iteration are reduced employing WinBUGS specific computational tricks.
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Bayesian Analysis for Penalized Spline Regression Using Win BUGS
2004Co-Authors: Ciprian M. Crainiceanu, David Ruppert, Matt P. WandAbstract:Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian Analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian Analysis in WinBUGS.
David Ruppert - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Analysis for penalized spline regression using winbugs
Journal of Statistical Software, 2005Co-Authors: Ciprian M. Crainiceanu, David Ruppert, Matt P. WandAbstract:Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian Analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian Analysis in WinBUGS. Good mixing properties of the MCMC chains are obtained by using low-rank thin-plate splines, while simulation times per iteration are reduced employing WinBUGS specific computational tricks.
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Bayesian Analysis for Penalized Spline Regression Using Win BUGS
2004Co-Authors: Ciprian M. Crainiceanu, David Ruppert, Matt P. WandAbstract:Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian Analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian Analysis in WinBUGS.
Ciprian M. Crainiceanu - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Analysis for penalized spline regression using winbugs
Journal of Statistical Software, 2005Co-Authors: Ciprian M. Crainiceanu, David Ruppert, Matt P. WandAbstract:Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian Analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian Analysis in WinBUGS. Good mixing properties of the MCMC chains are obtained by using low-rank thin-plate splines, while simulation times per iteration are reduced employing WinBUGS specific computational tricks.
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Bayesian Analysis for Penalized Spline Regression Using Win BUGS
2004Co-Authors: Ciprian M. Crainiceanu, David Ruppert, Matt P. WandAbstract:Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian Analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian Analysis in WinBUGS.
Jacinto Martín - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Analysis of finite mixtures of multinomial and negative-multinomial distributions
Computational Statistics & Data Analysis, 2007Co-Authors: M. J. Rufo, Carlos J. Pérez, Jacinto MartínAbstract:The Bayesian implementation of finite mixtures of distributions has been an area of considerable interest within the literature. Computational advances on approximation techniques such as Markov chain Monte Carlo (MCMC) methods have been a keystone to Bayesian Analysis of mixture models. This paper deals with the Bayesian Analysis of finite mixtures of two particular types of multidimensional distributions: the multinomial and the negative-multinomial ones. A unified framework addressing the main topics in a Bayesian Analysis is developed for the case with a known number of component distributions. In particular, theoretical results and algorithms to solve the label-switching problem are provided. An illustrative example is presented to show that the proposed techniques are easily applied in practice.
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An overview of robust Bayesian Analysis
Test, 1994Co-Authors: James O Berger, Elías Moreno, Luis Raul Pericchi, M. Jesús Bayarri, José M. Bernardo, Juan A. Cano, Julián Horra, Jacinto Martín, David Ríos-insúa, Bruno BetròAbstract:Robust Bayesian Analysis is the study of the sensitivity of Bayesian answers to uncertain inputs. This paper seeks to provide an overview of the subject, one that is accessible to statisticians outside the field. Recent developments in the area are also reviewed, though with very uneven emphasis.
James O Berger - One of the best experts on this subject based on the ideXlab platform.
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the case for objective Bayesian Analysis
Bayesian Analysis, 2006Co-Authors: James O BergerAbstract:Bayesian statistical practice makes extensive use of versions of ob- jective Bayesian Analysis. We discuss why this is so, and address some of the criticisms that have been raised concerning objective Bayesian Analysis. The dan- gers of treating the issue too casually are also considered. In particular, we suggest that the statistical community should accept formal objective Bayesian techniques with confldence, but should be more cautious about casual objective Bayesian techniques.
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An overview of robust Bayesian Analysis
Test, 1994Co-Authors: James O Berger, Elías Moreno, Luis Raul Pericchi, M. Jesús Bayarri, José M. Bernardo, Juan A. Cano, Julián Horra, Jacinto Martín, David Ríos-insúa, Bruno BetròAbstract:Robust Bayesian Analysis is the study of the sensitivity of Bayesian answers to uncertain inputs. This paper seeks to provide an overview of the subject, one that is accessible to statisticians outside the field. Recent developments in the area are also reviewed, though with very uneven emphasis.
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Bayesian Analysis for the Poly-Weibull Distribution
Journal of the American Statistical Association, 1993Co-Authors: James O Berger, Dongchu SunAbstract:Abstract In this article Bayesian Analysis for a Poly-Weibull distribution using informative priors is discussed. This distribution typically arises when the data is the minimum of several Weibull failure times from competing risks. To perform the Bayesian computations, simulation using the Gibbs sampler is suggested. This can be used to find posterior moments, the marginal posterior probability density function, and the predictive risk or reliability.
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ockham s razor and Bayesian Analysis
American Scientist, 1992Co-Authors: James O Berger, William H JefferysAbstract:'Ockham's razor', the ad hoc principle enjoining the greatest possible simplicity in theoretical explanations, is presently shown to be justifiable as a consequence of Bayesian inference; Bayesian Analysis can, moreover, clarify the nature of the 'simplest' hypothesis consistent with the given data. By choosing the prior probabilities of hypotheses, it becomes possible to quantify the scientific judgment that simpler hypotheses are more likely to be correct. Bayesian Analysis also shows that a hypothesis with fewer adjustable parameters intrinsically possesses an enhanced posterior probability, due to the clarity of its predictions.
