Results 101 to 110 of about 15,911,760 (295)
Advanced Engineering Materials, EarlyView.We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang +2 more
wiley +1 more source
Bayesian Averaging, Prediction and Nonnested Model Selection [PDF]
This paper studies the asymptotic relationship between Bayesian model averaging and post-selection frequentist predictors in both nested and nonnested models.
Han Hong, Bruce Preston
core
Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying
Advanced Engineering Materials, EarlyView.An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara +8 more
wiley +1 more source
Bayesian model selection for group studies — Revisited
NeuroImage, 2014 In this paper, we revisit the problem of Bayesian model selection (BMS) at the group level. We originally addressed this issue in Stephan et al. (2009), where models are treated as random effects that could differ between subjects, with an unknown population distribution.
Rigoux, Lionel +3 more
openaire +4 more sources
VAR Forecasting Using Bayesian Variable Selection [PDF]
This paper develops methods for automatic selection of variables in Bayesian vector autoregressions (VARs) using the Gibbs sampler. In particular, I provide computationally efficient algorithms for stochastic variable selection in generic linear and ...
Dimitris Korobilis
core
Advanced Engineering Materials, EarlyView.This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer +4 more
wiley +1 more source
Bayesian curve estimation by model averaging [PDF]
, 2003 A bayesian approach is used to estimate a nonparametric regression model. The main features of the procedure are, first, the functional form of the curve is approximated by a mixture of local polynomials by Bayesian Model Averaging (BMA); second, the ...
Peña, Daniel, Redondas, María Dolores
core +1 more source
Bayesian Model Selection with Graph Structured Sparsity
J. Mach. Learn. Res., 2019 We propose a general algorithmic framework for Bayesian model selection. A spike-and-slab Laplacian prior is introduced to model the underlying structural assumption. Using the notion of effective resistance, we derive an EM-type algorithm with closed-form iterations to efficiently explore possible candidates for Bayesian model selection.
Youngseok Kim, Chao Gao
openaire +4 more sources
A Hierarchical Bayesian Model for Frame Representation [PDF]
, 2010 In many signal processing problems, it is fruitful to represent the signal under study in a frame. If a probabilistic approach is adopted, it becomes then necessary to estimate the hyperparameters characterizing the probability distribution of the frame
Amel Benazza-Benyahia +9 more
core +1 more source