B. Betro, Bayesian robustness: theory and computation, in: F. Ruggeri, R.S. Kenett, F.W. Faltin (Eds.), Encyclopedia of Statistics in Quality and Reliability, Wiley, 2007, pp. 203-207.Betro, B. (2007) Bayesian robustness: theory and computation. In Ruggeri F, Kenett RS and Faltin ...
Boosting is a new, powerful method for classification. It is an iterative procedure which successively classifies a weighted version of the sample, and then reweights this sample dependent on how successful the classification was. In this paper we review some of the commonly used methods for perfo...
During the last decade, Bayesian probability theory has emerged as a framework in cognitive science and neuroscience for describing perception, reasoning and learning of mammals. However, our understanding of how probabilistic computations could be organized in the brain, and how the observed connectivity...
Moreover, the Median model is derived assuming Gaussian noise and thus the theory does not apply, for instance, to classification problems. 3 Overfitting and selection induced bias As discussed in Sect. 2.1, the performance of a model is usually defined in terms of the expected utility (2)....
Bayesian Approach to Global Optimization--Theory and Applications.by Jonas Mockus RBCGE Boender - 《Mathematics of Computation》 被引量: 0发表: 1991年 Bayesian Approach to Global Optimization J. Mockus, Bayesian Approach to Global Optimization: Theory and Applications (Kluwer, Dordrecht/Boston/London...
Springer Series in Statistics(共48册),这套丛书还有 《Statistics for High-Dimensional Data》《Monte Carlo Methods in Bayesian Computation (Springer Series in Statistics)》《The Science of Bradley Efron》《Sequential Analysis》《Bayes Theory (Springer Series in Statistics)》等。 喜欢读"Statistical Decis...
Bayesian structural equation modeling: a more flexible representation of substantive theory. Psychol. Methods 17, 313–335 (2012). Google Scholar van de Schoot, R. et al. Facing off with Scylla and Charybdis: a comparison of scalar, partial, and the novel possibility of approximate measurement...
In this way, Bayesian theory provides a unique and powerful structure for making inferences based on current and updated observed data. It continuously incorporates prior knowledge to update beliefs as new information emerges. These prior distributions can be broadly categorized into two groups: ...
approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as well as how it contrasts with the conventional approach. The theory is built up step by step, and important notion...
Keming,Alhamzawi,Rahim - 《Journal of Statistical Computation & Simulation》 被引量: 7发表: 2015年 RIDGE ESTIMATION OF INDEPENDENT POISSON MEANS UNDER ENTROPY LOSS Summary: {\\it A. E. Hoerl} and {\\it R. W. Kennard} [Technometrics 12, 55-67 (1970; Zbl 0202.172)] originally introduced...