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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Bayessche Statistik
von
Über diesen Kurs
Familiarity with calculus-based probability, principles of maximum-likelihood estimation, and Bayesian estimation.
Kompetenzen, die Sie erwerben
- Markov Model
- Bayesian Statistics
- Mixture Model
- R Programming
Familiarity with calculus-based probability, principles of maximum-likelihood estimation, and Bayesian estimation.
von

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.
Lehrplan - Was Sie in diesem Kurs lernen werden
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.
Maximum likelihood estimation for Mixture Models
Bayesian estimation for Mixture Models
Applications of Mixture Models
Bewertungen
- 5 stars70 %
- 4 stars23,33 %
- 3 stars6,66 %
Top-Bewertungen von BAYESIAN STATISTICS: MIXTURE MODELS
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.
Definitely quite mathematical in nature. Good way to learn about expectation-maximisation algorithm.
Über den Spezialisierung Bayessche Statistik
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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