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.
This course is part of the Bayesian Statistics Specialization
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Bayesian Statistics SpecializationUniversity of California, Santa CruzAbout this Course
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Course 3 of 5 in the
Intermediate Level
Familiarity with calculus-based probability, principles of maximum-likelihood estimation, and Bayesian estimation.
Approx. 22 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
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.
Course 3 of 5 in the
Intermediate Level
Familiarity with calculus-based probability, principles of maximum-likelihood estimation, and Bayesian estimation.
Approx. 22 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
Offered by
University of California, Santa Cruz
UC Santa Cruz is an outstanding public research university with a deep commitment to undergraduate education. It’s a place that connects people and programs in unexpected ways while providing unparalleled opportunities for students to learn through hands-on experience.
Syllabus - What you will learn from this course
4 hours to complete
Basic concepts on Mixture Models
This module defines mixture models, discusses its properties, and develops the likelihood function for a random sample from a mixture model that will be the basis for statistical learning.
4 hours to complete
9 videos (Total 94 min), 7 readings, 9 quizzes
4 hours to complete
Maximum likelihood estimation for Mixture Models
4 hours to complete
4 videos (Total 73 min), 2 readings, 2 quizzes
4 hours to complete
Bayesian estimation for Mixture Models
4 hours to complete
6 videos (Total 84 min), 2 readings, 2 quizzes
5 hours to complete
Applications of Mixture Models
5 hours to complete
7 videos (Total 108 min), 3 readings, 3 quizzes
Reviews
- 5 stars66.66%
- 4 stars24.24%
- 3 stars9.09%
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.
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.
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