# Bayesian Methods: Advanced Bayesian Computation Model

Bayesian statistics    |    Intermediate
• 11 videos | 51m 26s
• Includes Assessment
Rating 4.3 of 4 users (4)
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.

## WHAT YOU WILL LEARN

• 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

## IN THIS COURSE

• In this video, you will learn how to build and implement Bayesian linear regression models using Python.
• 3.  Hierarchical Linear Model
After completing this video, you will be able to list the prominent hierarchical linear models from the perspective of regression coefficients.
• 4.  Probability Model
Upon completion of this video, you will be able to describe the concept of probability models and illustrate the use of Bayesian methods for problems with missing data.
• 5.  Building Probability Models
In this video, you will learn how to build probability models using Python.
• 6.  Non-Linear Model
After completing this video, you will be able to describe non-linear and non-parametric models from the perspective of coefficient shrinkage and multivariate regression.
• 7.  Gaussian Process
After completing this video, you will be able to specify the fundamental concepts of Gaussian process models.
• 8.  Mixture Model
After completing this video, you will be able to recognize the approaches of using mixture models for classification and regression.
• 9.  Dirichlet Process Model
In this video, you will define and list the essential properties of Dirichlet processes.
• 10.  Bayesian Modeling with PyMC3
In this video, you will learn how to implement Bayesian inference models in Python using PyMC3.
• 11.  Exercise: Implement Bayesian models
Upon completion of this video, you will be able to 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.

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