Bayesian Statistics: Capstone Project

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Bayesian Statistics: Capstone Project

This course is part of Bayesian Statistics Specialization

Instructor: Jizhou Kang

1,719 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

10 reviews

Advanced level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

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.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

6 assignments¹

AI Graded see disclaimer

Taught in English

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This course is part of the Bayesian Statistics Specialization

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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 videosTotal 26 minutes

Introduction4 minutes
Model Formulation13 minutes
Prediction for AR Models9 minutes

7 readingsTotal 70 minutes

Prerequisite skill checklist10 minutes
Read Data10 minutes
Review: Useful Distributions10 minutes
Posterior Distribution Derivation10 minutes
AR model fitting example10 minutes
AR model prediction example10 minutes
Extended AR model10 minutes

2 assignmentsTotal 60 minutes

Practice Quiz for Week 130 minutes
First step for the project30 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

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

What's included

4 videos3 readings2 assignments

4 videosTotal 45 minutes

Prediction for Location Mixture of AR Models5 minutes
Full conditional distributions of model parameters26 minutes
Coding the Gibbs sampler9 minutes
Prediction for location mixture of AR model5 minutes

3 readingsTotal 30 minutes

Sample code for the Gibbs sampler10 minutes
Determine the number of components10 minutes
Location and scale mixture of AR model10 minutes

2 assignmentsTotal 60 minutes

Fit a location mixture of AR model30 minutes
Determine number of components for the mixture model30 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

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Instructor

Jizhou Kang

University of California, Santa Cruz

1 Course1,719 learners

Offered by

University of California, Santa Cruz

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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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