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.
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Bayesian Statistics: Mixture Models
University of California, Santa CruzAbout this Course
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Advanced Level
Approx. 21 hours to complete
English
Subtitles: English
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 21 hours to complete
English
Subtitles: English
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
9 videos
Installing and using R5m
Basic definitions25m
Mixtures of Gaussians10m
Zero-inflated mixtures11m
Hierarchical representations7m
Sampling from a mixture model5m
The likelihood function14m
Parameter identifiability10m
7 readings
An Introduction to R45m
Example of a bimodal mixture of Gaussians3m
Example of a unimodal and skewed mixture of Gaussians3m
Example of a unimodal, symmetric and heavy tailed mixture of Gaussians3m
Example of a zero-inflated negative binomial distribution3m
Example of a zero-inflated log Gaussian distribution3m
Sample code for simulating from a Mixture Model10m
7 practice exercises
Basic definitions6m
Mixtures of Gaussians4m
Zero-inflated distributions4m
Definition of Mixture Models20m
The likelihood function
Identifiability
Likelihood function for mixture models4m
4 hours to complete
Maximum likelihood estimation for Mixture Models
4 hours to complete
4 videos (Total 73 min), 2 readings, 2 quizzes
4 videos
EM for location mixtures of Gaussians22m
EM example 112m
EM example 213m
2 readings
Sample code for EM example 110m
Sample code for EM example 210m
4 hours to complete
Bayesian estimation for Mixture Models
4 hours to complete
6 videos (Total 84 min), 2 readings, 2 quizzes
6 videos
Markov Chain Monte Carlo algorithms, part 213m
MCMC for location mixtures of normals Part 119m
MCMC for location mixtures of normals Part 214m
MCMC Example 111m
MCMC Example 211m
2 readings
Sample code for MCMC example 110m
Sample code for MCMC example 210m
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 Example10m
Mixture Models for Clustering23m
Clustering example11m
Mixture Models and naive Bayes classifiers21m
Linear and quadratic discriminant analysis in the context of Mixture Models18m
Classification example10m
3 readings
Sample code for density estimation problems10m
Sample EM algorithm for clustering problems10m
Sample EM algorithm for classification problems10m
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