Bayesian Statistics: Mixture Models

This course is part of Bayesian Statistics Specialization

Instructor: Abel Rodriguez

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

  •   Markov Model
  •   Unsupervised Learning
  •   R Programming
  •   Mathematical Modeling
  •   Statistical Machine Learning
  •   Statistical Methods
  •   Machine Learning Algorithms
  •   Statistical Modeling
  •   Bayesian Statistics
  •   Probability & Statistics
  • There are 5 modules in this course

    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.

    Maximum likelihood estimation for Mixture Models

    Bayesian estimation for Mixture Models

    Applications of Mixture Models

    Practical considerations

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