Sanaelotfi/Bayesian_model_comparison
Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".
This project provides experimental code for researchers and machine learning practitioners who are evaluating different statistical models. It helps you understand the nuances of using marginal likelihood for tasks like model selection and hyperparameter tuning, specifically for deep neural networks. By running these experiments, you can analyze various models and gain insights into their generalization capabilities on unseen data.
No commits in the last 6 months.
Use this if you are a machine learning researcher or practitioner needing to rigorously compare different models, select the best architecture, or tune hyperparameters for deep learning models, and want to understand the theoretical and practical implications of using marginal likelihood.
Not ideal if you are looking for a plug-and-play tool for immediate model comparison without delving into the underlying theoretical evaluations or if you are not working with Bayesian methods.
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36
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4
Language
Jupyter Notebook
License
MIT
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Last pushed
Jun 16, 2022
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