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.

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