University of California, Santa Cruz

Bayesian Statistics: Mixture Models

This course is part of Bayesian Statistics Specialization

Abel Rodriguez

Instructor: Abel Rodriguez

9,953 already enrolled

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Gain insight into a topic and learn the fundamentals.
4.5

(55 reviews)

Intermediate level

Recommended experience

21 hours to complete
3 weeks at 7 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.5

(55 reviews)

Intermediate level

Recommended experience

21 hours to complete
3 weeks at 7 hours a week
Flexible schedule
Learn at your own pace

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.

Details to know

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Assessments

11 assignments

Taught in English

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This course is part of the Bayesian Statistics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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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

Instructor ratings
4.7 (25 ratings)
Abel Rodriguez
University of California, Santa Cruz
1 Course9,953 learners

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4.5

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Reviewed on Apr 13, 2023

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