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Bayesian Statistics

Duke University via Coursera

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  • Provider Coursera
  • $ Cost Free Online Course (Audit)
  • Session Upcoming
  • Language English
  • Certificate Paid Certificate Available
  • Start Date
  • Duration 5 weeks long

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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."

Taught by

Mine Çetinkaya-Rundel, Dr. David Banks, Dr. Colin Rundel and Dr. Merlise A Clyde

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Reviews for Coursera's Bayesian Statistics
2.7 Based on 9 reviews

  • 5 star 0%
  • 4 stars 22%
  • 3 stars 22%
  • 2 stars 56%
  • 1 star 0%

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  • 1
Farsan R
2.0 a year ago
by Farsan dropped this course.
This is the last course of specialization "Statistics with R". The first three courses were excellent but surprisingly this last course is a complete disappointment. I dropped the course after failing the first quiz multiple times even i carefully followed the lecture videos. Quiz questions were too complex (at least for me) based on the lecture videos. Previous three course contained a reading section that was very helpful but this course does not have that reading section part. Course instructor mine cetinkaya rundel was good at delivering lectures as always but as i could not relate quiz questions with video lectures.
4 people found
this review helpful
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Michael H
4.0 10 months ago
by Michael is taking this course right now.
This is definitely a challenging course. However, I took in that spirit and am really enjoying it so far. 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. I haven't done the rest of the specialisation, but did do the earlier stand-alone course fronted by Mine Cetinkaya (an absolutely brilliant lecturer).

The material and the pace is such that most of the lectures alone are not enough in one go to deliver understanding. How many lectures are? But you can watch t…
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Anonymous
2.0 a year ago
Anonymous dropped this course.
This looks like a half-cooked course. 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. As of November of 2016, it still needs some polishing.
1 person found
this review helpful
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Anonymous
3.0 a year ago
Anonymous is taking this course right now.
Concepts are abruptly introduced with no apparent thread. The instructors present these as if they are obvious.

This leaves the student scratching their heads.
1 person found
this review helpful
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José N
3.0 a year ago
by José completed this course, spending 10 hours a week on it and found the course difficulty to be hard.
This course is a real challenge for those with no backgrounds in Statistics. The course is the fourth in the framework of a five-course specialisation (Statistics with R) and, the general impression, is that this course is not balanced with the remainder of the specialisation. The three first courses are easy to follow, and there is book in addition to further understanding. This fourth course, on the contrary, lacks the appropiate materials and the video lectures are noticeably harder. I would only encourage you to enroll it if you feel confident with Statistics, probability, set theory,...
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T W
4.0 a year ago
T completed this course, spending 4 hours a week on it and found the course difficulty to be medium.
I have a background in applied statistics and I thought this course was pretty challenging. I'm not sure how this course was deemed to be appropriate for a beginner because I think I would have been frustrated and confused if I did not have prior familiarity with the topic.

I thought the discussions comparing the frequentist approach and outcomes to Bayesian techniques was most useful. Using R to do Bayesian modeling was something I wanted to learn how to do but the modeling techniques, diagnostics, and plots are easily transferable to SAS programming.
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Anonymous
2.0 a year ago
Anonymous partially completed this course.
Well, I find the lectures to be good, but the quizes are at times confusing and especially the last course. Sometimes using confusion to filter our learners to classify them according to bell curve can be drastic if too much confusion existed.
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Stephane M
2.0 2 years ago
by Stephane completed this course.
0 person found
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Alex I
2.0 2 years ago
Alex audited this course.
0 person found
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  • 1

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