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.
Bayesian Statistics: Capstone Project
Bayesian Statistics: Capstone Project
This course is part of Bayesian Statistics Specialization
Instructor: Jizhou Kang
Sponsored by BrightStar Care
Gain insight into a topic and learn the fundamentals.
Advanced level
Recommended experience
11 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Demonstrate a wide range of skills and knowledge in Bayesian statistics.
Explain essential concepts in Bayesian statistics.
Apply what you know to real-world data.
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
6 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 4 modules in this course
In this module, we will introduce conjugate Bayesian analysis for the autoregressive (AR) models.
What's included
3 videos7 readings2 assignments
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.
What's included
2 videos2 readings2 assignments
In this module, we will perform Bayesian analysis for location mixture of AR(p) models.
What's included
4 videos3 readings2 assignments
In this module, we will use everything we have learned up until now to perform a mixture model on time series data.
What's included
1 reading1 peer review
Instructor
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