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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 1891–1900 of 2050 papers

TitleStatusHype
Selective Inference and Learning Mixed Graphical Models—0
Factorized Asymptotic Bayesian Inference for Factorial Hidden Markov Models—0
Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model—0
A simple application of FIC to model selection—0
Information-based inference for singular models and finite sample sizes: A frequentist information criterion—0
Generalized Additive Model Selection—0
Data-Driven Learning of the Number of States in Multi-State Autoregressive Models—0
Bayesian Adaptive Matrix Factorization With Automatic Model Selection—0
Automatic Relevance Determination For Deep Generative Models—0
Bootstrapped Adaptive Threshold Selection for Statistical Model Selection and Estimation—0
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