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

This 11-video course explores advanced Bayesian computation models, as well as how to implement Bayesian modeling with linear regression, nonlinear, probabilistic, and mixture models. In addition, learners discover how to implement Bayesian inference models with PyMC3. First, learn how to build and implement Bayesian linear regression models by using Python for machine learning solutions. Examine prominent hierarchical linear models from the perspective of regression coefficients. Then view the concept of probability models and use of Bayesian methods for problems with missing data. You will discover how to build probability models by using Python, and examine coefficient shrinkage with nonlinear models, nonparametric models, and multivariate regression from nonlinear models. Examine fundamental concepts of Gaussian process models; the approaches of classification with mixture models and regression with mixture models; and essential properties of Dirichlet process models. Finally, learn how to implement Bayesian inference models in Python with PyMC3. The concluding exercise recalls hierarchical linear models from the perspective of regression coefficients, and asks learners to describe the approach of working with generalized linear models, and implement Bayesian inference by 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