florent-leclercq/Bayes_InfoTheory
Lectures on Bayesian statistics and information theory
This collection of Jupyter notebooks helps scientists and researchers understand and apply advanced statistical and machine learning techniques. It covers Bayesian statistics, information theory, and various sampling methods. Researchers can input their datasets and learn to perform tasks like signal de-noising, parameter inference, and model comparison, receiving insights for data analysis and decision-making.
Use this if you are a graduate student, researcher, or practitioner in fields like astrophysics, physics, or data science, seeking to deepen your understanding and practical application of Bayesian methods and information theory.
Not ideal if you are looking for a plug-and-play software tool for immediate data analysis without diving into the underlying mathematical and statistical concepts.
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Last pushed
Mar 15, 2026
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