Save for later

Bayesian Statistics

Statistics with R,

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."

Get Details and Enroll Now

OpenCourser is an affiliate partner of Coursera.

Get a Reminder

Send to:
Rating 3.1 based on 161 ratings
Length 6 weeks
Effort 5 weeks of study, 5-7 hours/week
Starts Oct 12 (last week)
Cost $79
From Duke University via Coursera
Instructors Mine Çetinkaya-Rundel, David Banks, Colin Rundel, Merlise A Clyde
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

Get a Reminder

Send to:

Similar Courses

What people are saying

bayesian statistics

As well as Bayesian statistics, you can learn R/markdown through the very well constructed labs and the advanced, but really helpful extra pdfs put out by Merlise Clyde.

Projects seemed designed for someone with a better grasp of R. I will probably look for another course on Bayesian statistics, because I feel my grasp of these concepts is still weak.

I only recommend this course to people who have sound knowledge in calculus and some background knowledge in Bayesian Statistics.

Very comprehensive course on Bayesian Statistics.

A good sampler of topics related to Bayesian Statistics.

I do hope that there will more MOOC's teaching Bayesian statistics soon.

The course is too sketchy: it does not provide enough materials to grasp the main ideas of Bayesian Statistics nor it gives any details about some formulas and important principles.This course does not have a book to follow along as the previous courses had (statistics).I had to spend more than 2 months to be able to understand all the concepts that this course was trying to teach.

Read more

other courses

It has everything to be an excellent course, like the quality of the other courses from the same group, but fails to deliver a correct learning experience.

Explanations not simplified as compared to the other courses in the specialisation.

Not as good as other courses in this specialization.

There is really no excuse for this; it's possible to provide an intuitive understanding of the Bayesian approach that is on par with the other courses (e.g.

This course delved too deep in the math that were not always explained as good as the other courses in this specialization.

Really liked the prof from the other courses (Mine), she really explained well...

Didn't have a good reference book that we could refer to like the other courses.

Read more

first three courses

The first three courses were excellent but surprisingly this last course is a complete disappointment.

I think the Professors involved are super-smart and more than just qualified, but the teaching method is a noted departure from the first three courses in this series.

The first three courses in this Duke series were superbly well done.

I appreciate very much what the Duke faculty achieved in the first three courses, but the treatment of Bayesian statistics that I have just suffered through was shameful.

There seems to be a significant disconnect between the first three courses (probability, inference, linear regression) and the fourth course (bayesian).

The first three courses in this specialization are very good, but somehow this course are way below the quality of the previous ones.

The first three courses were very good; you had the benefit of the free textbook that comes with them.

Read more

hard to follow

good stuff but extremely hard to follow, not engaging at all.

They are hard to follow, erratic, lack thoroughness and are incomplete.

The last two weeks are way too hard to follow and could provide more practical examples instead of focusing on mathematical theory and formulas.

The lectures themselves can be hard to follow and often times skip over important calculations.

Really hard to follow and finish, especially compared to the other classes in this specialization.

The videos are hard to follow.

Read more

too much

Sometimes using confusion to filter our learners to classify them according to bell curve can be drastic if too much confusion existed.

Ended up s The professors tried to put too much material into very short videos.

There is FAR too much here to be covered in a single module.

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

Read more

rather than

Most of the times the focus was to teach the method of performing a Bayesian Statistical process rather than teaching the actual concept.

This course has a much steeper learning curve than the first three, and goes from theory to examples in action rather than vice versa.

When material is beyond the scope of what perspective students can reasonably be expected to understand, faculty members should be honest enough to just say so rather than pretending to teach the subject matter.

I answered some of the quiz questions based on intuition and what looked reasonable rather than actually knowing how to solve them.

Read more

Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

LPN Prior Authorizations $45k

Supervisor Prior Authorization Nurse Manager $50k

Prior Authorizations RN $55k

Prior Authorization and Outpatient Scheduling Specialist $59k

Senior Prior Authorization Representative $66k

Supervisor Prior Authorization Rep $67k

Prior Authorization Specialist Contractor $71k

Senior Prior Authorization Rep $79k

Prior Authorization Project Manager $91k

Project Manager (prior Engagement Manager) $94k

Senior Prior Authorization Specialist $99k

Senior Prior Authorization/UM Resolution $135k

Write a review

Your opinion matters. Tell us what you think.

Rating 3.1 based on 161 ratings
Length 6 weeks
Effort 5 weeks of study, 5-7 hours/week
Starts Oct 12 (last week)
Cost $79
From Duke University via Coursera
Instructors Mine Çetinkaya-Rundel, David Banks, Colin Rundel, Merlise A Clyde
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

Similar Courses

Sorted by relevance

Like this course?

Here's what to do next:

  • Save this course for later
  • Get more details from the course provider
  • Enroll in this course
Enroll Now