- Level Expert
- Duration 12 hours
- Course by University of California, Santa Cruz
-
Offered by
About
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.Modules
Week 1
2
Assignment
- Practice Quiz for Week 1
- First step for the project
3
Videos
- Introduction
- Model Formulation
- Prediction for AR Models
7
Readings
- Prerequisite skill checklist
- Read Data
- Review: Useful Distributions
- Posterior Distribution Derivation
- AR model fitting example
- AR model prediction example
- Extended AR model
Week 2
2
Assignment
- Determine the order of your data
- Calculate DIC for single AR model
2
Videos
- AIC and BIC in selecting the order of AR process
- Deviance information criterion (DIC)
2
Readings
- AIC and BIC example
- DIC Example
Week 3
2
Assignment
- Fit a location mixture of AR model
- Determine number of components for the mixture model
4
Videos
- Prediction for Location Mixture of AR Models
- Full conditional distributions of model parameters
- Coding the Gibbs sampler
- Prediction for location mixture of AR model
3
Readings
- Sample code for the Gibbs sampler
- Determine the number of components
- Location and scale mixture of AR model
Week 4
1
Peer Review
- Peer-graded Assignment: Data Analysis Project
1
Readings
- Acknowledgments and Reference
Auto Summary
Embark on UC Santa Cruz's Bayesian Statistics: Capstone Project, an expert-level course in Data Science & AI offered by Coursera. This comprehensive project allows you to showcase your Bayesian statistics skills by analyzing real-world data. Through lecture videos, quizzes, and a detailed report, you'll solidify your understanding in 720 minutes. Ideal for advanced learners, this course is accessible with a Starter subscription.

Jizhou Kang