Bayesian Methods: Advanced Bayesian Computation Model

### Bayesian Methods: Advanced Bayesian Computation Model

Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level

Overview/Description

Explore advanced Bayesian computation models, as well as how to implement Bayesian models using linear regression, non-linear, probabilistic, and mixture models. In addition, discover how to implement Bayesian inference models using PyMC3.

Expected Duration (hours)
0.9

Lesson Objectives

Bayesian Methods: Advanced Bayesian Computation Model

• discover the key concepts covered in this course
• demonstrate how to build and implement Bayesian linear regression models using Python
• list the prominent hierarchical linear models from the perspective of regression coefficients
• describe the concept of probability models and illustrate the use of Bayesian methods for problems with missing data
• demonstrate how to build probability models using Python
• describe non-linear and non-parametric models from the perspective of coefficient shrinkage and multivariate regression
• specify the fundamental concepts of Gaussian process models
• recognize the approaches of using mixture models for classification and regression
• define and list the essential properties of Dirichlet process models
• demonstrate how to implement Bayesian inference models in Python with PyMC3
• recall hierarchical linear models from the perspective of regression coefficients, describe the approach of working with generalized linear models, and implement Bayesian inference using PyMC3
• Course Number:
it_mlbmmldj_03_enus

Expertise Level
Intermediate