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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 1601–1610 of 2050 papers

TitleStatusHype
Bayesian Active Model Selection with an Application to Automated Audiometry—0
Bayesian Adaptive Matrix Factorization With Automatic Model Selection—0
Bayesian Anomaly Detection and Classification—0
Bayesian Boosting for Linear Mixed Models—0
Bayesian CART models for insurance claims frequency—0
A Bayesian Model for Bivariate Causal Inference—0
Bayesian Evidence and Model Selection—0
Bayesian Hierarchical Community Discovery—0
Bayesian high-dimensional linear regression with generic spike-and-slab priors—0
Bayesian Interpolation with Deep Linear Networks—0
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