The focus of this paper is to replace GP-based machine learning models with other, potentially more adaptive and flexible, Bayesian models. More specifically, we explore Bayesian spline-based models and Bayesian ensemble-learning methods as surrogate models in a BO setting. Bayesian multivariate adapt...
Advanced simulation methodsBayesian model updatingKriging approximationReduced-order modelsTransitional Markov chain Monte CarloThis work explores the feasibility of integrating an adaptive meta-model into a finite element model updating formulation using dynamic response data. A Bayesian model updating approach...
widespread penetration of Bayesian methods into subject domains over the last 20 years or so: people can fit models and make inferences that were previously impossible or very cumbersome. And this is where this book wins hands down, since adaptive trials are so natural, ethical and efficient...
(2010). Adaptive mixture model- ing Metropolis methods for Bayesian analysis of nonlinear state-space models. J. Comput. Graph. Statist. 19 260-280. MR2758305Niemi, Jarad, and Mike West, 2010, Adaptive mixture modeling metropolis methods for bayesian analysis of nonlinear state-space models, ...
As described in Section 4.2.3., the Bayesian methods can use sample data to adjust the posterior probability distribution of parameters in pavement performance models, which provides a good framework for model parameter updating. The Bayesian updating makes iterative use of Bayesian inference [104,181...
Millar, R.B.: Comparison of hierarchical Bayesian models for overdispersed count data using DIC and Bayes’ factors. Biometrics 65, 962–969 (2009) Article MathSciNet MATH Google Scholar Min, C.-K., Zellner, A.: Bayesian and non-Bayesian methods for combining models and forecasts with app...
formally incorporated into statistical evaluation, Bayesian methods explicitly quantify the otherwise implicit influ- ence of clinical judgment and prior beliefs on the interpretation of trial results. 在临床试验的统计分析中,常规的频率主...
2002) combining the MARS methodology with the benefits of Bayesian methods for accounting for model uncertainty to achieve improvements in predictive performance. In implementation of the Bayesian MARS approach, Markov chain Monte Carlo methods are used for computations, in which at each iteration of ...
There have been three main approaches developed to consider directly the grouping structure in classification/regression models based on LASSO penalties: 1. The first approach is to develop methods that directly consider the grouping structure or the correlation among features in classification and ...
However, such separable models do not work in more complex settings, such as those involving effects of cortical processing (Webster & Wilson, 2000). So far, adaptive estimation procedures have only been applied to 1D psychometric functions. Although many methods can estimate multiple parameters of...