scholarly journals MODEL SELECTION FOR FUNCTIONAL MIXED MODEL VIA GAUSSIAN PROCESS REGRESSION

2014 ◽  
Vol 46 ◽  
pp. 23-35 ◽  
Author(s):  
Toshihiro Misumi
Keyword(s):  
Model Selection ◽  
Gaussian Process ◽  
Mixed Model ◽  
Gaussian Process Regression ◽  
Selection For

Model Selection for Gaussian Process Regression

2017 ◽  
pp. 306-318 ◽  
Author(s):  
Nico S. Gorbach ◽  
Andrew An Bian ◽  
Benjamin Fischer ◽  
Stefan Bauer ◽  
Joachim M. Buhmann
Keyword(s):  
Model Selection ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Selection For

Bayesian site selection for fast Gaussian process regression

2014 ◽  
Vol 46 (5) ◽  
pp. 543-555 ◽  
Author(s):  
Arash Pourhabib ◽  
Faming Liang ◽  
Yu Ding
Keyword(s):  
Gaussian Process ◽  
Site Selection ◽  
Gaussian Process Regression ◽  
Selection For

scholarly journals Model comparison of $$\Lambda $$CDM vs $$R_h=ct$$ using cosmic chronometers

2020 ◽  
Vol 80 (8) ◽  
Author(s):  
Haveesh Singirikonda ◽  
Shantanu Desai
Keyword(s):  
Model Selection ◽  
Gaussian Process ◽  
Bayesian Model ◽  
Model Comparison ◽  
Gaussian Process Regression ◽  
Acoustic Oscillations ◽  
Bayesian Model Comparison ◽  
Baryon Acoustic Oscillations ◽  
Best Fit

Abstract In 2012, Bilicki and Seikel (Mon Not R Astron Soc 425:1664, 2012) showed that H(z) data reconstructed using Gaussian Process Regression from cosmic chronometers and baryon acoustic oscillations, conclusively rules out the $$R_h=ct$$Rh=ct model. These results were disputed by Melia and collaborators in two different works (Melia and Maier in Mon Not R Astron Soc 432:2669, 2013; Melia and Yennapureddy in JCAP 2018:034, 2018), who showed using both an unbinned analysis and Gaussian Process reconstructed H(z) data from chronometers, that $$R_h=ct$$Rh=ct is favored over $$\Lambda $$ΛCDM model. To resolve this imbroglio, we carry out model comparison of $$\Lambda $$ΛCDM versus $$R_h=ct$$Rh=ct by independently reproducing the above claims using the latest chronometer data. We perform model selection between these two models using Bayesian model comparison. We find that no one model between $$\Lambda $$ΛCDM and $$R_h=ct$$Rh=ct is decisively favored when uniform priors on $$\Lambda $$ΛCDM parameters are used. However, if we use priors centered around the Planck best-fit values, then $$\Lambda $$ΛCDM is very strongly preferred over $$R_h=ct$$Rh=ct.

Two-Layer Gaussian Process Regression With Example Selection for Image Dehazing

2017 ◽  
Vol 27 (12) ◽  
pp. 2505-2517 ◽  
Author(s):  
Xin Fan ◽  
Yi Wang ◽  
Xianxuan Tang ◽  
Renjie Gao ◽  
Zhongxuan Luo
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Image Dehazing ◽  
Selection For
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