The Experts below are selected from a list of 326208 Experts worldwide ranked by ideXlab platform
James L. Beck - One of the best experts on this subject based on the ideXlab platform.
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calculation of posterior probabilities for Bayesian Model class assessment and averaging from posterior samples based on dynamic system data
Computer-aided Civil and Infrastructure Engineering, 2010Co-Authors: Sai Hung Cheung, James L. BeckAbstract:In recent years, Bayesian Model updating techniques based on dynamic data have been applied in system identification and structural health monitoring. Because of Modeling uncertainty, a set of competing candidate Model classes may be available to represent a system and it is then desirable to assess the plausibility of each Model class based on system data. Bayesian Model class assessment may then be used, which is based on the posterior probability of the different candidates for representing the system. If more than one Model class has significant posterior probability, then Bayesian Model class averaging provides a coherent mechanism to incorporate all of these Model classes in making probabilistic predictions for the system response. This Bayesian Model assessment and averaging requires calculation of the evidence for each Model class based on the system data, which requires the evaluation of a multi-dimensional integral involving the product of the likelihood and prior defined by the Model class. In this article, a general method for calculating the evidence is proposed based on using posterior samples from any Markov Chain Monte Carlo algorithm. The effectiveness of the proposed method is illustrated by Bayesian Model updating and assessment using simulated earthquake data from a ten-story nonclassically damped building responding linearly and a four-story building responding inelastically.
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Algorithms for Bayesian Model Class Selection of Higher-dimensional Dynamic Systems
Volume 1: 21st Biennial Conference on Mechanical Vibration and Noise Parts A B and C, 2007Co-Authors: Sai Hung Cheung, James L. BeckAbstract:n recent years, Bayesian Model updating techniques based on measured data have been applied in structural health monitoring. Often we are faced with the problem of how to select the `best' Model from a set of competing candidate Model classes for the system based on data. To tackle this problem, Bayesian Model class selection is used, which provides a rigorous Bayesian updating procedure to give the probability of different candidate classes for a system, based on the data from the system. There may be cases where more than one Model class has significant probability and each of these will give different predictions. Bayesian Model class averaging provides a coherent mechanism to incorporate all the considered Model classes in the probabilistic predictions for the system. However, both Bayesian Model class selection and Bayesian Model class averaging require the calculation of the evidence of the Model class which requires the nontrivial computation of a multi-dimensional integral. In this paper, several methods for solving this computationally challenging problem of Model class selection are presented, proposed and compared. The efficiency of the proposed methods is illustrated by an example involving a structural dynamic system.
Luis R Pericchi - One of the best experts on this subject based on the ideXlab platform.
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Intrinsic Priors for Objective Bayesian Model Selection
Advances in Econometrics, 2014Co-Authors: Elías Moreno, Luis R PericchiAbstract:Abstract We put forward the idea that for Model selection the intrinsic priors are becoming a center of a cluster of a dominant group of methodologies for objective Bayesian Model Selection. The intrinsic method and its applications have been developed in the last two decades, and has stimulated closely related methods. The intrinsic methodology can be thought of as the long searched approach for objective Bayesian Model selection and hypothesis testing. In this paper we review the foundations of the intrinsic priors, their general properties, and some of their applications.
Daniel Sabanés Bové - One of the best experts on this subject based on the ideXlab platform.
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Objective Bayesian Model selection for Cox regression.
Statistics in medicine, 2016Co-Authors: Leonhard Held, Isaac Gravestock, Daniel Sabanés BovéAbstract:There is now a large literature on objective Bayesian Model selection in the linear Model based on the g-prior. The methodology has been recently extended to generalized linear Models using test-based Bayes factors. In this paper, we show that test-based Bayes factors can also be applied to the Cox proportional hazards Model. If the goal is to select a single Model, then both the maximum a posteriori and the median probability Model can be calculated. For clinical prediction of survival, we shrink the Model-specific log hazard ratio estimates with subsequent calculation of the Breslow estimate of the cumulative baseline hazard function. A Bayesian Model average can also be employed. We illustrate the proposed methodology with the analysis of survival data on primary biliary cirrhosis patients and the development of a clinical prediction Model for future cardiovascular events based on data from the Second Manifestations of ARTerial disease (SMART) cohort study. Cross-validation is applied to compare the predictive performance with alternative Model selection approaches based on Harrell's c-Index, the calibration slope and the integrated Brier score. Finally, a novel application of Bayesian variable selection to optimal conditional prediction via landmarking is described. Copyright © 2016 John Wiley & Sons, Ltd.
Xiaofeng Shao - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Model selection based on parameter estimates from subsamples
Statistics & Probability Letters, 2013Co-Authors: Jingsi Zhang, Wenxin Jiang, Xiaofeng ShaoAbstract:We propose Bayesian Model selection based on composite datasets, which can be constructed from various subsample estimates. The method remains consistent without fully specifying a probability Model, and is useful for dependent data, when asymptotic variance of the parameter estimator is difficult to estimate.
Sai Hung Cheung - One of the best experts on this subject based on the ideXlab platform.
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calculation of posterior probabilities for Bayesian Model class assessment and averaging from posterior samples based on dynamic system data
Computer-aided Civil and Infrastructure Engineering, 2010Co-Authors: Sai Hung Cheung, James L. BeckAbstract:In recent years, Bayesian Model updating techniques based on dynamic data have been applied in system identification and structural health monitoring. Because of Modeling uncertainty, a set of competing candidate Model classes may be available to represent a system and it is then desirable to assess the plausibility of each Model class based on system data. Bayesian Model class assessment may then be used, which is based on the posterior probability of the different candidates for representing the system. If more than one Model class has significant posterior probability, then Bayesian Model class averaging provides a coherent mechanism to incorporate all of these Model classes in making probabilistic predictions for the system response. This Bayesian Model assessment and averaging requires calculation of the evidence for each Model class based on the system data, which requires the evaluation of a multi-dimensional integral involving the product of the likelihood and prior defined by the Model class. In this article, a general method for calculating the evidence is proposed based on using posterior samples from any Markov Chain Monte Carlo algorithm. The effectiveness of the proposed method is illustrated by Bayesian Model updating and assessment using simulated earthquake data from a ten-story nonclassically damped building responding linearly and a four-story building responding inelastically.
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Algorithms for Bayesian Model Class Selection of Higher-dimensional Dynamic Systems
Volume 1: 21st Biennial Conference on Mechanical Vibration and Noise Parts A B and C, 2007Co-Authors: Sai Hung Cheung, James L. BeckAbstract:n recent years, Bayesian Model updating techniques based on measured data have been applied in structural health monitoring. Often we are faced with the problem of how to select the `best' Model from a set of competing candidate Model classes for the system based on data. To tackle this problem, Bayesian Model class selection is used, which provides a rigorous Bayesian updating procedure to give the probability of different candidate classes for a system, based on the data from the system. There may be cases where more than one Model class has significant probability and each of these will give different predictions. Bayesian Model class averaging provides a coherent mechanism to incorporate all the considered Model classes in the probabilistic predictions for the system. However, both Bayesian Model class selection and Bayesian Model class averaging require the calculation of the evidence of the Model class which requires the nontrivial computation of a multi-dimensional integral. In this paper, several methods for solving this computationally challenging problem of Model class selection are presented, proposed and compared. The efficiency of the proposed methods is illustrated by an example involving a structural dynamic system.
