This is the capstone project for UC Santa Cruz's Bayesian Statistics Specialization. It is an opportunity for you to demonstrate a wide range of skills and knowledge in Bayesian statistics and to apply what you know to real-world data. You will review essential concepts in Bayesian statistics with lecture videos and quizzes, and you will perform a complex data analysis and compose a report on your methods and results.
This course is part of the Bayesian Statistics Specialization
Offered By
Bayesian Statistics SpecializationUniversity of California, Santa CruzAbout this Course
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Course 5 of 5 in the
Advanced Level
Familiarity with calculus-based probability, maximum-likelihood estimation, Bayesian inference, mixture models, and time-series analysis.
Approx. 12 hours to complete
English
Subtitles: English
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 5 of 5 in the
Advanced Level
Familiarity with calculus-based probability, maximum-likelihood estimation, Bayesian inference, mixture models, and time-series analysis.
Approx. 12 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
3 hours to complete
Bayesian Conjugate Analysis for Autogressive Time Series Models
In this module, we will introduce conjugate Bayesian analysis for the autoregressive (AR) models.
3 hours to complete
3 videos (Total 26 min), 7 readings, 2 quizzes
2 hours to complete
Model Selection Criteria
In this module, we will introduce some criteria that can be used in selecting the order of AR processes and the number of mixing components, which will be used later when we introduce mixture of AR models.
2 hours to complete
2 videos (Total 20 min), 2 readings, 2 quizzes
2 hours to complete
Bayesian location mixture of AR(P) model
In this module, we will perform Bayesian analysis for location mixture of AR(p) models.
2 hours to complete
4 videos (Total 45 min), 3 readings, 2 quizzes
5 hours to complete
Peer-reviewed data analysis project
In this module, we will use everything we have learned up until now to perform a mixture model on time series data.
5 hours to complete
1 reading
About the Bayesian Statistics Specialization
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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