Description
This course is a comprehensive guide to Bayesian Statistics. It includes video explanations along with real life illustrations, examples, numerical problems, and take away notes. The course covers the basic theory behind probabilistic and Bayesian modelling, and their applications to common problems in data science, business, and applied sciences.
The course is divided into the following sections:
Section 2 and 3: These two sections cover the concepts that are crucial to understand the basics of Bayesian Statistics-
- Introduction to Bayesian Probability
- Introduction to PyMC3 primer
- Summarizing the posterior.
- Introduction to ROPE.
- introduction to Gaussian.
- Student’s t-distribution.
- Hierarchical models Introduction.
- Linear models and high autocorrelation.
- Introduction to Pearson coefficient from a multivariate Gaussian.
- Robust linear regression.
- Hierarchical linear regression.
- Correlation, causation, and the messiness of life.
- Polynomial regression.
- Introduction to Confounding variables and redundant variables.
- Masking effect variables.
- Adding interactions.
- Variable variance.
