Back to Bayesian Statistics

stars

747 ratings

•

242 reviews

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."...

RR

Sep 20, 2017

Great course. Difficult to apprehend sometimes as the Frequentist paradigm is learned first but once you get it, it is really amazing to see the believe update in action with data.

GH

Apr 9, 2018

I like this course a lot. Explanations are clear and much of the (unnecessarily heavyweight) maths is glossed over. I particularly liked the sections on Bayesian model selection.

Filter by:

By Bram M

•Aug 25, 2020

Great course, with good examples of how Bayesian stats work. It is incredibly difficult to follow, however, and would benefit from a more elaborate discussion of methodology (e.g. how (conjugate) priors are chosen, how to report results, etc). It currently is one long list of formulae, formulae, and formulae

By Uira d M

•Jul 3, 2019

The course is well structured but the span of topics is large and the complexity great. Maybe an extended version with more explanations and demonstrations of the equations would be better for understanding the whole concept of bayesian statistics, specially inference.

By De'Varus M

•Feb 14, 2019

Though this section in the specialization is a little more difficult than the other sections. The supplemental material provided is helpful in navigating through the course. I will continue to read through this material to further my understanding of the material.

By Francisco M

•Nov 9, 2019

The course is very interesting, but the jump from the previous course is too large. From calculating probabilities by hand and understanding the odds involved, to integrate distributions is too abrupt and not explained in detail.

By Janio A M

•Aug 9, 2018

Its a tough course however, I will just suggest to focus more on the practical side of things after doing all the theory. I really enjoyed course 4 where the professor used "R" to compare different models using the crime dataset.

By zhaokai

•Aug 16, 2016

I hope we can have access to the slides, and this can save attendees a lot of time, because I think after we finished watching the video we can skip relevant slides when we come across problems in doing exercise.

By Mark F C

•Jun 21, 2018

It was a good course, though I would include more coursework and exercises in R to assist with comprehending a difficult subject. Overall, good course for something that's difficult to teach.

By Kian B

•Jul 29, 2016

The section about Beta-Binomial Conjugate is taught very fast and unless the student is quite familiar with Beta and Gamma distributions, it makes it very difficult to follow the course.

By JeaSung P

•Jul 18, 2017

This is one of many good courses that one can get a glimpse of Bayesian statistics though it lacks of thorough explanation of mathematical background and reading materials of any kind.

By José L E N

•Jan 3, 2017

Theis course is substantially more difficult than the three first ones, and the material is scarce. However, I must admit that this is one of the courses I have ever learnt the most

By Stanley R C

•Jan 29, 2018

The instructors have great expertise, but this course is pretty difficult for a Bayesian newbie. Additional study guides would be helpful (especially week 4).

By Lalu P L

•Jun 2, 2019

The course could have been more comprehensive and less verbose. It had so much content in a tiny course. Content should be less and more comprehensive.

By Malolan S

•Sep 10, 2019

A bit more depth in explaining conjugacy in priors and posteriors will be very helpful. A possible way would be to have more example illustrations.

By Ángela D C

•Jun 12, 2018

Week 3 was too much information too soon, but week 4 was great again like the other courses in this specialisation. Learned so much, thanks!

By KALYESUBULA M

•Jun 3, 2017

Learnt a lot. Though the subject material was hard to grasp first hand, it is good that instructor was readily available to help us through.

By Adam A

•Aug 25, 2017

An interesting and challenging course, would be better with more real examples and explanation as some of the material felt rushed

By Marwa A E K M A Z

•Jan 7, 2020

It's a good one, but not as previous courses. Week 3 isn't well explained as other weeks. Hope it can be further improved

By Hanyu Z

•Dec 8, 2016

The material is good. However, there is no support from the instructors to answer our questions in the discussion forum.

By Niels R

•Jul 6, 2019

This course through the material too fast. The content should have been spread out over two courses in my opinion.

By Emmanouil K

•Aug 16, 2017

This is a very interesting topic. Lectures in weeks 3 and 4 could use some work.

By Vicken A

•Dec 28, 2016

Bayesian stats is a broad topic. Learners would benefit from more material.

By Raja F Z

•May 23, 2020

this Course very informative and bears an applied approach for learning.

By Jaime R

•Nov 8, 2018

Theorethical backdrop is a bit excessive on an R focused course

By Elham L

•Aug 25, 2020

The material was interesting, yet required more time.

By Liew H P

•Jan 16, 2019

This course is challenging and well-presented!

- Finding Purpose & Meaning in Life
- Understanding Medical Research
- Japanese for Beginners
- Introduction to Cloud Computing
- Foundations of Mindfulness
- Fundamentals of Finance
- Machine Learning
- Machine Learning Using Sas Viya
- The Science of Well Being
- Covid-19 Contact Tracing
- AI for Everyone
- Financial Markets
- Introduction to Psychology
- Getting Started with AWS
- International Marketing
- C++
- Predictive Analytics & Data Mining
- UCSD Learning How to Learn
- Michigan Programming for Everybody
- JHU R Programming
- Google CBRS CPI Training

- Natural Language Processing (NLP)
- AI for Medicine
- Good with Words: Writing & Editing
- Infections Disease Modeling
- The Pronounciation of American English
- Software Testing Automation
- Deep Learning
- Python for Everybody
- Data Science
- Business Foundations
- Excel Skills for Business
- Data Science with Python
- Finance for Everyone
- Communication Skills for Engineers
- Sales Training
- Career Brand Management
- Wharton Business Analytics
- Penn Positive Psychology
- Washington Machine Learning
- CalArts Graphic Design

- Professional Certificates
- MasterTrack Certificates
- Google IT Support
- IBM Data Science
- Google Cloud Data Engineering
- IBM Applied AI
- Google Cloud Architecture
- IBM Cybersecurity Analyst
- Google IT Automation with Python
- IBM z/OS Mainframe Practitioner
- UCI Applied Project Management
- Instructional Design Certificate
- Construction Engineering and Management Certificate
- Big Data Certificate
- Machine Learning for Analytics Certificate
- Innovation Management & Entrepreneurship Certificate
- Sustainabaility and Development Certificate
- Social Work Certificate
- AI and Machine Learning Certificate

- Computer Science Degrees
- Business Degrees
- Public Health Degrees
- Data Science Degrees
- Bachelor's Degrees
- Bachelor of Computer Science
- MS Electrical Engineering
- Bachelor Completion Degree
- MS Management
- MS Computer Science
- MPH
- Accounting Master's Degree
- MCIT
- MBA Online
- Master of Applied Data Science
- Global MBA
- Master's of Innovation & Entrepreneurship
- MCS Data Science
- Master's in Computer Science
- Master's in Public Health