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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 821–830 of 2050 papers

TitleStatusHype
Gaussian Process-based Spatial Reconstruction of Electromagnetic fields—0
Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria—0
Fixed effects testing in high-dimensional linear mixed models—0
Choice modelling in the age of machine learning - discussion paper—0
A Rule-Based Epidemiological Modelling Framework—0
General Hannan and Quinn Criterion for Common Time Series—0
A Critical Review of Large Language Models: Sensitivity, Bias, and the Path Toward Specialized AI—0
A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: application to porcine aortic valves—0
Convergence Rates of Variational Inference in Sparse Deep Learning—0
Fitting very flexible models: Linear regression with large numbers of parameters—0
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