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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 1611–1620 of 2050 papers

TitleStatusHype
Bayesian Learning with Wasserstein Barycenters—0
Bayesian leave-one-out cross-validation for large data—0
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors—0
Bayesian model selection consistency and oracle inequality with intractable marginal likelihood—0
Bayesian Model Selection for Change Point Detection and Clustering—0
Bayesian Model Selection for Identifying Markov Equivalent Causal Graphs—0
Bayesian Model Selection Methods for Mutual and Symmetric k-Nearest Neighbor Classification—0
Bayesian Model Selection of Stochastic Block Models—0
Bayesian Model Selection via Mean-Field Variational Approximation—0
Bayesian Network Models for Adaptive Testing—0
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