Bayesian Methods: Advanced Bayesian Computation Model
Bayesian statistics
11 Videos | 55m 56s - Includes Assessment
- Earns a Badge
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
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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
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1.Course Overview1m 45sUP NEXT
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2.Bayesian Model and Linear Regression4m 7s
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3.Hierarchical Linear Model8m 21s
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4.Probability Model7m 22s
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5.Building Probability Models4m
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6.Non-Linear Model6m 2s
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7.Gaussian Process3m 23s
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8.Mixture Model4m 28s
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9.Dirichlet Process Model5m 14s
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10.Bayesian Modeling with PyMC33m 36s
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11.Exercise: Implement Bayesian models3m 8s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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