Results 101 to 110 of about 15,712,923 (297)
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
Advanced Materials, EarlyView.This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
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 Materials, EarlyView.ABSTRACT The accelerating expansion of data‐centric technologies is sharply increasing the energy burden of information storage, placing unprecedented pressure on the efficiency of magnetic switching. Conventional field‐driven reversal, once the foundation of magnetic memory, has become impractical in modern architectures due to its high energy cost ...
Mohammad H. Badarneh +2 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
Advanced Materials, EarlyView.The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj +8 more
wiley +1 more source
BELMM: Bayesian model selection and random walk smoothing in time-series clustering. [PDF]
Bioinformatics, 2023 Sarala O, Pyhäjärvi T, Sillanpää MJ.
europepmc +1 more source