Bayesian Statistics: Techniques and Models
This course is part of Bayesian Statistics Specialization.
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.
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What you'll learn
Implement MCMC methods for complex Bayesian models
Develop and assess hierarchical statistical models
Use R and JAGS for advanced statistical computing
Apply Bayesian techniques to real-world data analysis
Master predictive distribution and model comparison
Communicate statistical results effectively
Skills you'll gain
This course includes:
7.7 Hours PreRecorded video
17 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This advanced course in Bayesian statistics focuses on sophisticated modeling techniques and computational methods. Students learn to implement Markov chain Monte Carlo (MCMC) methods, construct hierarchical models, and use tools like R and JAGS for complex statistical analysis. The curriculum covers linear regression, ANOVA, logistic regression, and Poisson models, culminating in a hands-on data analysis project.
Statistical modeling and Monte Carlo estimation
Module 1 · 3 Hours to complete
Markov chain Monte Carlo (MCMC)
Module 2 · 4 Hours to complete
Common statistical models
Module 3 · 5 Hours to complete
Count data and hierarchical modeling
Module 4 · 5 Hours to complete
Capstone project
Module 5 · 10 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Bayesian Statistics Specialization
Instructor
Doctoral Student in Statistics at UC Santa Cruz Specializing in Bayesian Techniques and Models
Matthew Heiner is a doctoral student in Statistics at the University of California, Santa Cruz. His academic focus includes Bayesian statistics, specifically techniques and models that enhance statistical analysis
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