Bayesian Statistics: Capstone Project
Master advanced Bayesian statistics through practical application in this comprehensive capstone project focused on real-world data analysis.
Master advanced Bayesian statistics through practical application in this comprehensive capstone project focused on real-world data analysis.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Bayesian Statistics Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Apply Bayesian statistical methods to real-world data analysis
Implement conjugate analysis for autoregressive models
Develop and evaluate mixture models for time series data
Master model selection criteria and computational methods
Create comprehensive statistical analysis reports
Skills you'll gain
This course includes:
1.5 Hours PreRecorded video
6 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This capstone project course provides an advanced application of Bayesian statistical methods through hands-on data analysis. Students work with autoregressive time series models, implementing conjugate Bayesian analysis and mixture models. The curriculum covers model selection criteria, deviance information criterion, and practical implementation through computational methods. The course culminates in a comprehensive data analysis project demonstrating mastery of Bayesian statistical concepts.
Bayesian Conjugate Analysis for Autogressive Time Series Models
Module 1 · 2 Hours to complete
Model Selection Criteria
Module 2 · 1 Hours to complete
Bayesian location mixture of AR(P) model
Module 3 · 2 Hours to complete
Peer-reviewed data analysis project
Module 4 · 5 Hours to complete
Fee Structure
Instructor
Emerging Scholar in Statistics with a Focus on Bayesian Methods and Machine Learning
Jizhou Kang is a doctoral student in Statistics at the University of California, Santa Cruz, having joined the department in 2019 after completing his M.S. in Applied Mathematics and Statistics at Johns Hopkins University. His research interests encompass Bayesian nonparametric methods, ordinal regression, high-dimensional data analysis, models for longitudinal data, causal inference, and machine learning.
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