Results 21 to 30 of about 396,010 (290)
Latent Profile Analysis of Human Values: What is the Optimal Number of Clusters?
Methodology, 2021 Latent Profile Analysis (LPA) is a method to extract homogeneous clusters characterized by a common response profile. Previous works employing LPA to human value segmentation tend to select a small number of moderately homogeneous clusters based on model
Mikkel N. Schmidt +6 more
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Inverted Weibull Regression Models and Their Applications
Stats, 2021 In this paper, we propose the classical and Bayesian regression models for use in conjunction with the inverted Weibull (IW) distribution; there are the inverted Weibull Regression model (IW-Reg) and inverted Weibull Bayesian regression model (IW-BReg ...
Sarah R. Al-Dawsari, Khalaf S. Sultan
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Feature Selection in High-Dimensional Models via EBIC with Energy Distance Correlation
Entropy, 2022 In this paper, the LASSO method with extended Bayesian information criteria (EBIC) for feature selection in high-dimensional models is studied. We propose the use of the energy distance correlation in place of the ordinary correlation coefficient to ...
Isaac Xoese Ocloo, Hanfeng Chen
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BELMKN: Bayesian Extreme Learning Machines Kohonen Network
Algorithms, 2018 This paper proposes the Bayesian Extreme Learning Machine Kohonen Network (BELMKN) framework to solve the clustering problem. The BELMKN framework uses three levels in processing nonlinearly separable datasets to obtain efficient clustering in terms of ...
J. Senthilnath +4 more
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Entropy, 2021 Model selection criteria are widely used to identify the model that best represents the data among a set of potential candidates. Amidst the different model selection criteria, the Bayesian information criterion (BIC) and the Akaike information criterion
Luca Spolladore +3 more
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Nuclear data adjustment using Bayesian inference, diagnostics for model fit and influence of model parameters [PDF]
EPJ Web of Conferences, 2020 The mathematical models used for nuclear data evaluations contain a large number of theoretical parameters that are usually uncertain. These parameters can be calibrated (or improved) by the information collected from integral/differential experiments ...
Kumar D. +4 more
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Information criteria for astrophysical model selection [PDF]
, 2007 Model selection is the problem of distinguishing competing models, perhaps featuring different numbers of parameters. The statistics literature contains two distinct sets of tools, those based on information theory such as the Akaike Information ...
Liddle, Andrew R
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Deviance Information Criteria for Model Selection in Approximate Bayesian Computation [PDF]
Statistical Applications in Genetics and Molecular Biology, 2011 Approximate Bayesian computation (ABC) is a class of algorithmic methods in Bayesian inference using statistical summaries and computer simulations. ABC has become popular in evolutionary genetics and in other branches of biology. However, model selection under ABC algorithms has been a subject of intense debate during the recent years.
Francois, Olivier, Laval, Guillaume
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Pakistan Journal of Statistics and Operation Research, 2023 In this academic work a comparison between a Bayesian-Structural Equation Modelling (B-SEM) and a Partial Least Squares-Structural Equation Modelling (PLS-SEM) on a relationship amongst self-directed learning readiness (SDLR), E-learning readiness, and learning motivation of undergraduate students throughout the outbreak of Covid-19 is studied.
Reny Rian Marliana +2 more
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ADAPTIVE LEARNING MACHINES FOR NONLINEAR CLASSIFICATION AND BAYESIAN INFORMATION CRITERIA [PDF]
Bulletin of informatics and cybernetics, 2004 Regularization is a well-known method for the treatment of mathematically illposed problems. By using the method of regularization, we propose a new machine learning algorithm, adaptive learning machine, to classify the high-dimensional data with complex structure.
Tomohiro Ando +2 more
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