Course details

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