Introduction to Bayesian Analysis Course with Python 2021

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.

Section 4: This section covers Linear model generalization:

Introduction to Generalizing linear models.

Introduction to Logistic regression.

Applying the logistic regression to The Iris dataset.

Multiple logistic regression.

Interpreting the coefficients of a logistic regression.

Dealing with correlated variables.

Dealing with unbalanced classes.

Introduction to Softmax regression.

Introduction to Discriminative and generative models.

Introduction to Poisson regression.

Introduction to The zero–inflated Poisson model.