Bayesian Methods: Advanced Bayesian Computation Model

Bayesian statistics    |    Intermediate
  • 11 videos | 51m 26s
  • Includes Assessment
  • Earns a Badge
Rating 4.3 of 4 users 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

  • 1m 45s
  • 4m 7s
    In this video, you will learn how to build and implement Bayesian linear regression models using Python. FREE ACCESS
  • Locked
    3.  Hierarchical Linear Model
    8m 21s
    After completing this video, you will be able to list the prominent hierarchical linear models from the perspective of regression coefficients. FREE ACCESS
  • Locked
    4.  Probability Model
    7m 22s
    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. FREE ACCESS
  • Locked
    5.  Building Probability Models
    4m
    In this video, you will learn how to build probability models using Python. FREE ACCESS
  • Locked
    6.  Non-Linear Model
    6m 2s
    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. FREE ACCESS
  • Locked
    7.  Gaussian Process
    3m 23s
    After completing this video, you will be able to specify the fundamental concepts of Gaussian process models. FREE ACCESS
  • Locked
    8.  Mixture Model
    4m 28s
    After completing this video, you will be able to recognize the approaches of using mixture models for classification and regression. FREE ACCESS
  • Locked
    9.  Dirichlet Process Model
    5m 14s
    In this video, you will define and list the essential properties of Dirichlet processes. FREE ACCESS
  • Locked
    10.  Bayesian Modeling with PyMC3
    3m 36s
    In this video, you will learn how to implement Bayesian inference models in Python using PyMC3. FREE ACCESS
  • Locked
    11.  Exercise: Implement Bayesian models
    3m 8s
    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. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

YOU MIGHT ALSO LIKE

Book Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Book Bayesian Optimization: Theory and Practice Using Python
Book Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with Python

PEOPLE WHO VIEWED THIS ALSO VIEWED THESE

Course Linear Models & Gradient Descent: Managing Linear Models
Rating 3.8 of 8 users Rating 3.8 of 8 users (8)
Course Model Management: Building & Deploying Machine Learning Models in Production
Rating 4.1 of 11 users Rating 4.1 of 11 users (11)
Course Math for Data Science & Machine Learning
Rating 3.9 of 177 users Rating 3.9 of 177 users (177)