Bayesian Statistics
Learn to understand the Bayesian paradigm, and apply the Bayesian methods for solving practical problems with the Bayesian Statistics online course.
Intermediate
Online
3 Weeks
2,699 INR Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
Bayesian Statistics certification is a 3-week long programme where all the participants enrolling must devote approximately 10 hours of their time. This certification course is available on the Coursera platform but has been designed by Duke University. Also, this course is part 4 of 5 in the specialization of Statistics with R Specialization offering a free trial that is valid for 7 whole days. Whoever the interested participants must make sure that they have some kind of prior knowledge of what all is taught in the precious course parts of the specialization.
The Bayesian Statistics training will be responsible for introducing Bayesian comparisons related to means, credible regions, and Bayesian regression using multiple models to the participants. The course finally teaches all the applicants to learn the application of Bayesian methods to several practical issues and implementation in R programming. Apart from providing a certificate that is shareable, the candidates will be able to change the course timings with respect to their convenient schedules.
The Highlights
- 3 weeks programme
- 100% online course
- 10 hours of classes
- Learning by self
- Intermediate course level
- 10 subtitles available
- Certificate by Coursera
Programme Offerings
- Online Course
- Shareable Certificate
- Flexible Deadlines
- Graded Assignments
- English Course
- 10 Subtitles Available
- Lectures
- videos
- Readings
- Practice Exercises.
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | Coursera |
Bayesian Statistics Fee structure :
| Head | Amount in INR |
| Course fees | Rs. 2,699 |
Eligibility Criteria
Educational Qualification
Having some language knowledge of Spanish, French, English, Russian, Arabic, German, Italian, Portuguese (European), and Vietnamese will prove to be beneficial. Also, having some idea of the other courses in this specialization will be preferable.
Certification Qualifying Details
For the Bayesian Statistics certification by Coursera students should be attending all the quizzes, video lectures, and also reading that is available on the syllabus section.
What you will learn
The candidates from the Bayesian Statistics certification course can learn the following:
- They will learn to transform prior probabilities into posterior ones with the use of Bayes’ rule.
- Learners will be able to learn the perspective and theory of the Bayesian paradigm.
- Candidates will also build models by implementing R programming techniques.
Admission Details
Bayesian Statistics admission process is as follows:
Step 1: Visit the official website: https://www.coursera.org/learn/bayesian
Step 2: Clicking the ‘Enroll for free’ button on the website homepage.
Step 3: On this page, candidates will just have to make a participant account on Coursera.
Step 4: Then participants need to next find out financial aid if they require it after logging into Coursera using the account details made in the previous step.
Step 5: Finally, the above steps complete the admission process.
Application Details
No small or big application form filing is required. Only the candidates have to keep in mind that they need to fill up their details on a signup form on Coursera to get their accounts made.
The Syllabus
Videos
Introduction to Statistics with R
Readings
- About Statistics with R Specialization
- About Bayesian Statistics
- Pre-requisite Knowledge
- Special Thanks
Discussion Prompt
- Introduce Yourself
Videos
- 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
Readings
- Module Learning Objectives
- About Lab Choices
- Week 1 Lab Instructions (RStudio)
- Week 1 Lab Instructions (RStudio Cloud)
Assignments
- Week 1 Lab
- Week 1 Practice Quiz
- Week 1 Quiz
Discussion Prompt
- Introduce Yourself
Videos
- 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
Readings
- Module Learning Objectives
- Week 2 Lab Instructions (RStudio)
- Week 1 Lab Instructions (RStudio Cloud)
Assignments
- Week 2 Lab
- Week 2 Practice Quiz
- Week 2 Quiz
Videos
- 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?
Readings
- Module Learning Objectives
- Week 3 Lab Instructions (RStudio)
- Week 3 Lab Instructions (RStudio Cloud)
Assignments
- Week 3 Lab
- Week 3 Practice Quiz
- Week 3 Quiz
Videos
- 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
Readings
- Module Learning Objectives
- Week 4 Lab Instructions (RStudio Cloud)
- Week 4 Lab Instructions (RStudio Cloud)
Assignments
- Week 4 Lab
- Week 4 Practice Quiz
- Week 4 Quiz
Videos
- 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
Reading
About this module
Reading
Project information
Peer Review
- Data Analysis Project
Instructors
Duke University, Durham Frequently Asked Questions (FAQ's)
Coursera does not ask for the participant to be of a particular age for this programme,
Approximately 35 hours has to be managed by the participants for this programme.
Yes both the mobile application, and the Coursera website support specialization cancellation.
There isn’t an option for pausing, so the candidates must cancel, and then according to their subscription, and restart all over again.
Applicants can surely change the email addresses that they have provided for future communication.