Duke University - Bayesian Statistics
- Offered byCoursera
Bayesian Statistics at Coursera Overview
Duration | 35 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Bayesian Statistics at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 4 of 5 in the Statistics with R Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 35 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
Bayesian Statistics at Coursera Course details
- This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes? rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm. The course will apply Bayesian methods to several practical problems, to show end-to-end Bayesian analyses that move from framing the question to building models to eliciting prior probabilities to implementing in R (free statistical software) the final posterior distribution. Additionally, the course will introduce credible regions, Bayesian comparisons of means and proportions, Bayesian regression and inference using multiple models, and discussion of Bayesian prediction.
- We assume learners in this course have background knowledge equivalent to what is covered in the earlier three courses in this specialization: "Introduction to Probability and Data," "Inferential Statistics," and "Linear Regression and Modeling."
Bayesian Statistics at Coursera Curriculum
About the Specialization and the Course
Introduction to Statistics with R
About Statistics with R Specialization
About Bayesian Statistics
Pre-requisite Knowledge
Special Thanks
The Basics of Bayesian Statistics
Conditional Probabilities and Bayes' Rule
Bayes' Rule and Diagnostic Testing
Bayes Updating
Bayesian vs. frequentist definitions of probability
Inference for a Proportion: Frequentist Approach
Inference for a Proportion: Bayesian Approach
Effect of Sample Size on the Posterior
Frequentist vs. Bayesian Inference
Module Learning Objectives
About Lab Choices
Week 1 Lab Instructions (RStudio)
Week 1 Lab Instructions (RStudio Cloud)
Week 1 Lab
Week 1 Practice Quiz
Week 1 Quiz
Bayesian Inference
Bayesian Inference
From the Discrete to the Continuous
Elicitation
Conjugacy
Inference on a Binomial Proportion
The Gamma-Poisson Conjugate Families
The Normal-Normal Conjugate Families
Non-Conjugate Priors
Credible Intervals
Predictive Inference
Module Learning Objectives
Week 2 Lab Instructions (RStudio)
Week 1 Lab Instructions (RStudio Cloud)
Week 2 Lab
Week 2 Practice Quiz
Week 2 Quiz
Decision Making
Decision making
Losses and decision making
Working with loss functions
Minimizing expected loss for hypothesis testing
Posterior probabilities of hypotheses and Bayes factors
The Normal-Gamma Conjugate Family
Inference via Monte Carlo Sampling
Predictive Distributions and Prior Choice
Reference Priors
Mixtures of Conjugate Priors and MCMC
Hypothesis Testing: Normal Mean with Known Variance
Comparing Two Paired Means Using Bayes' Factors
Comparing Two Independent Means: Hypothesis Testing
Comparing Two Independent Means: What to Report?
Module Learning Objectives
Week 3 Lab Instructions (RStudio)
Week 3 Lab Instructions (RStudio Cloud)
Week 3 Lab
Week 3 Practice Quiz
Week 3 Quiz
Bayesian Regression
Bayesian regression
Bayesian simple linear regression
Checking for outliers
Bayesian multiple regression
Model selection criteria
Bayesian model uncertainty
Bayesian model averaging
Stochastic exploration
Priors for Bayesian model uncertainty
R demo: crime and punishment
Decisions under model uncertainty
Module Learning Objectives
Week 4 Lab Instructions (RStudio Cloud)
Week 4 Lab Instructions (RStudio Cloud)
Week 4 Lab
Week 4 Practice Quiz
Week 4 Quiz
Perspectives on Bayesian Applications
Bayesian inference: a talk with Jim Berger
Bayesian methods and big data: a talk with David Dunson
Bayesian methods in biostatistics and public health: a talk with Amy Herring
About this module
Project information
Bayesian Statistics at Coursera Admission Process
Important Dates
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