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.
Bayesian Statistics: Mixture Models
This course is part of Bayesian Statistics Specialization
Instructor: Abel Rodriguez
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What you'll learn
Explain the basic principles behind the algorithm for fitting a mixture model.
Compute the expectation and variance of a mixture distribution.
Use mixture models to solve classification and clustering problems, and to provide density estimates.
Skills you'll gain
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There are 5 modules in this course
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.
What's included
9 videos7 readings7 assignments2 peer reviews1 discussion prompt
What's included
4 videos2 readings2 peer reviews1 discussion prompt
What's included
6 videos2 readings2 peer reviews
What's included
7 videos3 readings3 peer reviews
What's included
7 videos5 readings4 assignments1 peer review1 discussion prompt
Instructor
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Recommended if you're interested in Data Analysis
University of California, Santa Cruz
University of California, Santa Cruz
University of California, Santa Cruz
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