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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 571–580 of 2050 papers

TitleStatusHype
Bayesian Network Models for Adaptive Testing—0
Bayesian Model Selection via Mean-Field Variational Approximation—0
An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction—0
Bayesian Model Selection of Stochastic Block Models—0
An information criterion for auxiliary variable selection in incomplete data analysis—0
Dominant Drivers of National Inflation—0
Bayesian Model Selection Methods for Mutual and Symmetric k-Nearest Neighbor Classification—0
Bayesian Model Selection for Identifying Markov Equivalent Causal Graphs—0
Bayesian Model Selection for Change Point Detection and Clustering—0
An Homotopy Algorithm for the Lasso with Online Observations—0
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