This course is part of the Bayesian Statistics Specialization

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stars
39 ratings

Abel Rodriguez

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Bayesian Statistics SpecializationUniversity of California, Santa Cruz

About this Course

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Bayesian Statistics: Mixture Models introduces you to an important class of statistical models. The course is organized in five modules, each of which contains lecture videos, short quizzes, background reading, discussion prompts, and one or more peer-reviewed assignments. Statistics is best learned by doing it, not just watching a video, so the course is structured to help you learn through application. 
Some exercises require the use of R, a freely-available statistical software package. A brief tutorial is provided, but we encourage you to take advantage of the many other resources online for learning R if you are interested.

This is an intermediate-level course, and it was designed to be the third in UC Santa Cruz's series on Bayesian statistics, after Herbie Lee's "Bayesian Statistics: From Concept to Data Analysis" and Matthew Heiner's "Bayesian Statistics: Techniques and Models." To succeed in the course, you should have some knowledge of and comfort with calculus-based probability, principles of maximum-likelihood estimation, and Bayesian estimation.

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Course 3 of 5 in the

Bayesian Statistics Specialization

Intermediate Level

Familiarity with calculus-based probability, principles of maximum-likelihood estimation, and Bayesian estimation.

Approx. 21 hours to complete

English

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Skills you will gain

Markov Model
Bayesian Statistics
Mixture Model
R Programming

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 3 of 5 in the

Bayesian Statistics Specialization

Intermediate Level

Familiarity with calculus-based probability, principles of maximum-likelihood estimation, and Bayesian estimation.

Approx. 21 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.75/5 (16 Ratings)

Abel Rodriguez

Professor

Applied Mathematics and Statistics

7,358 Learners

1 Course

Offered by

University of California, Santa Cruz

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Syllabus - What you will learn from this course

Week1

Week 1

4 hours to complete

Basic concepts on Mixture Models

4 hours to complete

9 videos (Total 94 min), 7 readings, 9 quizzes

9 videos

Welcome to Bayesian Statistics: Mixture Models3mInstalling and using R5mBasic definitions25mMixtures of Gaussians10mZero-inflated mixtures11mHierarchical representations7mSampling from a mixture model5mThe likelihood function14mParameter identifiability10m

7 readings

An Introduction to R45mExample of a bimodal mixture of Gaussians3mExample of a unimodal and skewed mixture of Gaussians3mExample of a unimodal, symmetric and heavy tailed mixture of Gaussians3mExample of a zero-inflated negative binomial distribution3mExample of a zero-inflated log Gaussian distribution3mSample code for simulating from a Mixture Model10m

7 practice exercises

Basic definitions6mMixtures of Gaussians4mZero-inflated distributions4mDefinition of Mixture Models20mThe likelihood functionIdentifiabilityLikelihood function for mixture models4m

Week2

Week 2

4 hours to complete

Maximum likelihood estimation for Mixture Models

4 hours to complete

4 videos (Total 73 min), 2 readings, 2 quizzes

Week3

Week 3

4 hours to complete

Bayesian estimation for Mixture Models

4 hours to complete

6 videos (Total 84 min), 2 readings, 2 quizzes

Week4

Week 4

5 hours to complete

Applications of Mixture Models

5 hours to complete

7 videos (Total 108 min), 3 readings, 3 quizzes

7 videos

Density estimation using Mixture Models12mDensity Estimation Example10mMixture Models for Clustering23mClustering example11mMixture Models and naive Bayes classifiers21mLinear and quadratic discriminant analysis in the context of Mixture Models18mClassification example10m

3 readings

Sample code for density estimation problems10mSample EM algorithm for clustering problems10mSample EM algorithm for classification problems10m

Reviews

4.6

14 reviews

5 stars
66.66%
4 stars
23.07%
3 stars
10.25%

TOP REVIEWS FROM BAYESIAN STATISTICS: MIXTURE MODELS

by CMMay 17, 2021

Definitely quite mathematical in nature. Good way to learn about expectation-maximisation algorithm.

by SMJan 19, 2021

I learned a lot about bayesian mixture model, expectation maximization, and MCMC algorithms and their use case in classification and clustering problems. I highly recommend this course.

View all reviews

About the Bayesian Statistics Specialization

This Specialization is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

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Bayesian Statistics: Mixture Models

About this Course

Skills you will gain