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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 1221–1230 of 2050 papers

TitleStatusHype
Stationary Geometric Graphical Model Selection—0
Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection—0
Statistical Inference for Pairwise Graphical Models Using Score Matching—0
Statistical inference for quantum singular models—0
Statistical inference of assortative community structures—0
Statistical Model Criticism of Variational Auto-Encoders—0
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning—0
Structural-constrained Methods for the Identification of Unobservable False Data Injection Attacks in Power Systems—0
Structural Learning of Multivariate Regression Chain Graphs via Decomposition—0
Structural Risk Minimization for Learning Nonlinear Dynamics—0
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