Bayesian Statistics: Capstone Project

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Bayesian Statistics: Capstone Project

This course is part of Bayesian Statistics Specialization

Instructor: Jizhou Kang

1,557 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

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.

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

R Programming

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

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

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

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

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Instructor

Jizhou Kang

University of California, Santa Cruz

1 Course 1,557 learners

Offered by

University of California, Santa Cruz

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Frequently asked questions

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