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Bayesian Statistics: Capstone Project

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Bayesian Statistics: Capstone Project

This course is part of Bayesian Statistics Specialization

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Gain insight into a topic and learn the fundamentals.
4.5

10 reviews

Advanced level

Recommended experience

1 week to complete
at 10 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

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Assessments

6 assignments¹

AI Graded see disclaimer
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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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

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.

In this module, we will introduce conjugate Bayesian analysis for the autoregressive (AR) models.

What's included

3 videos7 readings2 assignments

3 videos•Total 26 minutes
  • Introduction•4 minutes
  • Model Formulation•13 minutes
  • Prediction for AR Models•9 minutes
7 readings•Total 70 minutes
  • Prerequisite skill checklist•10 minutes
  • Read Data•10 minutes
  • Review: Useful Distributions•10 minutes
  • Posterior Distribution Derivation•10 minutes
  • AR model fitting example•10 minutes
  • AR model prediction example•10 minutes
  • Extended AR model•10 minutes
2 assignments•Total 60 minutes
  • Practice Quiz for Week 1•30 minutes
  • First step for the project•30 minutes

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

2 videos•Total 20 minutes
  • AIC and BIC in selecting the order of AR process•13 minutes
  • Deviance information criterion (DIC)•7 minutes
2 readings•Total 20 minutes
  • AIC and BIC example•10 minutes
  • DIC Example•10 minutes
2 assignments•Total 60 minutes
  • Determine the order of your data•30 minutes
  • Calculate DIC for single AR model•30 minutes

In this module, we will perform Bayesian analysis for location mixture of AR(p) models.

What's included

4 videos3 readings2 assignments

4 videos•Total 45 minutes
  • Prediction for Location Mixture of AR Models•5 minutes
  • Full conditional distributions of model parameters•26 minutes
  • Coding the Gibbs sampler•9 minutes
  • Prediction for location mixture of AR model•5 minutes
3 readings•Total 30 minutes
  • Sample code for the Gibbs sampler•10 minutes
  • Determine the number of components•10 minutes
  • Location and scale mixture of AR model•10 minutes
2 assignments•Total 60 minutes
  • Fit a location mixture of AR model•30 minutes
  • Determine number of components for the mixture model•30 minutes

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

1 reading•Total 10 minutes
  • Acknowledgments and Reference•10 minutes
1 peer review•Total 300 minutes
  • Peer-graded Assignment: Data Analysis Project•300 minutes

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Instructor

University of California, Santa Cruz
1 Course•1,725 learners

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Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.