Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...

0_data_generation.ipynb [Hidden] Data generation ⚠️ Not for initial use. This notebook simulates the kinetic dataset. Use it only if you want to inspect or regenerate the synthetic data.

This course introduces the theoretical, philosophical, and mathematical foundations of Bayesian Statistical inference. Students will learn to apply this foundational knowledge to real-world data ...

I recently came across this very hard looking and creatively articulated problem in an old entrance examination paper for Cambridge University. When I first looked at it, it seemed very complex. But ...

Bayesian methods are becoming an increasingly popular approach to data analysis across a wide range of research fields. They offer a flexible and structured framework for statistical inference, ...

Understanding how to update beliefs from evidence is a foundational skill in statistics and machine learning. This project gives you a practical beginner workflow where you can: specify prior beliefs ...

Approximate Bayesian computation (ABC) algorithms are a class of Monte Carlo methods for doing inference when the likelihood function can be simulated from, but not explicitly evaluated. This ...