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UC Santa Cruz - Bayesian Statistics: Capstone Project 

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  • Public/Government Institute

Bayesian Statistics: Capstone Project
 at 
Coursera 
Overview

Duration

12 hours

Total fee

Free

Mode of learning

Online

Official Website

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Credential

Certificate

Bayesian Statistics: Capstone Project
Table of content
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  • Overview
  • Highlights
  • Course Details
  • Curriculum

Bayesian Statistics: Capstone Project
 at 
Coursera 
Highlights

  • Earn a Certificate upon completion
Details Icon

Bayesian Statistics: Capstone Project
 at 
Coursera 
Course details

Skills you will learn
More about 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

Bayesian Statistics: Capstone Project
 at 
Coursera 
Curriculum

Bayesian Conjugate Analysis for Autogressive Time Series Models

Introduction

Model Formulation

Prediction for AR Models

Prerequisite skill checklist

Read Data

Review: Useful Distributions

Posterior Distribution Derivation

AR model fitting example

AR model prediction example

Extended AR model

Practice Quiz for Week 1

First step for the project

Model Selection Criteria

AIC and BIC in selecting the order of AR process

Deviance information criterion (DIC)

AIC and BIC example

DIC Example

Determine the order of your data

Calculate DIC for single AR model

Bayesian location mixture of AR(P) model

Prediction for Location Mixture of AR Models

Full conditional distributions of model parameters

Coding the Gibbs sampler

Prediction for location mixture of AR model

Sample code for the Gibbs sampler

Determine the number of components

Location and scale mixture of AR model

Fit a location mixture of AR model

Determine number of components for the mixture model

Peer-reviewed data analysis project

Acknowledgments and Reference

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Bayesian Statistics: Capstone Project
 at 
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