general regressioninput-output relationshipmodel selectionnonparametric modelingBayesian identification has attracted considerable interest in various research areas for the determination of the mathematical model with suitable complexity based on input-output measurements. Regression analysis is an important tool ...
Yuen and Ortiz [287] Multiresolution Bayesian nonparametric general regression method 20-storey shear building, and 3D truss. Cheung and Bansal [302] Gibbs based approach for Bayesian model updating 4-DOF mechanical system, 120-DOF. Frame structure Damagedetection Akhlaghi et al., [303] MCMC four...
Theoretical support for the use of the proposed methodology is provided by establishing strong consistency of a general nonparametric prior on G under simple sufficient conditions. Consistency is defined according to a L1-type distance on the space of choice probabilities and is achieved by extending ...
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However, if the true regression function is not of the form assumed, then the parametric approach can be grossly biased. We just assume that the regression surface is smooth, with the estimate of the function adapting to the true underlying functional form. This is called a nonparametric ...
In the last 20 years, a lot of achievements have been made in the study of posterior contraction rates of nonparametric Bayesian methods, and plenty of them involve sieve priors, but mainly for specific models or sieves. We provide a posterior contraction theorem for general parametric sieve ...
Due to the increasing availability of large data sets, the need for a general-purpose massively parallel analysis tool is becoming ever greater. Bayesian nonparametric mixture models, exemplified by the Dirichlet process mixture model (DPMM), provide a principled Bayesian approach to adapt model comple...
We develop a set of scalable Bayesian inference procedures for a general class of nonparametric regression models based on embarrassingly parallel MCMC. Specifically, we first perform independent nonparametric Bayesian inference on each subset split from a massive dataset, and then aggregate those results...
This is a direct result both of the nonparametric nature of the GP—the model adapts its complexity to the data—as well as the smoothing effects of the prior. Held-out test data from each participant yielded a median area under the curve (AUC) score of 94% (Fig. 2c). For ...
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