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In this course, you will :
- introduced to the fundamental concepts of probability and statistical distributions, as well as the famous Bayes' Theorem, which serves as the foundation of Bayesian methods
- To draw conclusions from randomised coin tosses, you'll create your first Bayesian model.
- Learn how to use Bayes' Theorem to estimate the parameters of probability distributions using the grid approximation technique, and how to update these estimates as new data becomes available.
- Before finally practising the important skill of reporting results to a non-technical audience, learn how to incorporate prior knowledge into the model.
Syllabus :
- The Bayesian way
- Bayesian estimation
- Bayesian inference
- Bayesian linear regression with pyMC3
