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Learner Reviews & Feedback for Bayesian Statistics: Techniques and Models by University of California, Santa Cruz

4.8
stars
470 ratings

About the Course

This is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through use of simple conjugate models. Real-world data often require more sophisticated models to reach realistic conclusions. This course aims to expand our “Bayesian toolbox” with more general models, and computational techniques to fit them. In particular, we will introduce Markov chain Monte Carlo (MCMC) methods, which allow sampling from posterior distributions that have no analytical solution. We will use the open-source, freely available software R (some experience is assumed, e.g., completing the previous course in R) and JAGS (no experience required). We will learn how to construct, fit, assess, and compare Bayesian statistical models to answer scientific questions involving continuous, binary, and count data. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. The lectures provide some of the basic mathematical development, explanations of the statistical modeling process, and a few basic modeling techniques commonly used by statisticians. Computer demonstrations provide concrete, practical walkthroughs. Completion of this course will give you access to a wide range of Bayesian analytical tools, customizable to your data....

Top reviews

JH

Oct 31, 2017

This course is excellent! The material is very very interesting, the videos are of high quality and the quizzes and project really helps you getting it together. I really enjoyed it!!!

CB

Feb 14, 2021

The course was really interesting and the codes were easy to follow. Although I did take the previous course for this series, I still found it hard to grasp the concepts immediately.

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51 - 75 of 157 Reviews for Bayesian Statistics: Techniques and Models

By Danial A

Jan 10, 2018

The best course I had in statistics. unlike many other courses the instructor does not ignore the underlying mathematics of the codes.

By Rishi R

Sep 1, 2020

One of the best practical math courses present in coursera. Loved the course and will surely look upto the next course eagerly.

By Sandra M D T S

Nov 20, 2023

Good Course which gives knowledge of Bayesian models and Techniques such as MCMC, metropolis hasting and their applications

By Wangtx

Dec 11, 2018

Great materials and well organized lecture structure. But in the meanwhile, it requires quite a lot preliminary knowledge.

By Dongxiao H

Nov 15, 2017

terrific, so I've learn quite a lot basic knowledge about MCMC. So I can build kinds of models with better understanding.

By Leonardo F

Apr 2, 2021

Very interesting.

I would like to have a follow on since the possible applications of the topics explained in the course.

By Manuel M S

Aug 20, 2020

Excellent course for introducing yourself to Monte Carlo Methods applied to Bayesian statistics. Highly recommended!

By Ahad H T

May 2, 2018

Outstanding, Excellent, Must do for statistician. I'm from Civil Engg Background easily capable to learn the course

By Russell N

Apr 27, 2020

Fantastic course that I was able to immediately incorporate into my work. Great mix of theory and hands on coding!

By Vlad

Mar 21, 2018

Very good course giving a good practical kickoff to a very interesting and exciting topic of Bayesian statistics.

By Bill B

Jun 20, 2020

Very useful introduction to practical application of Bayesian inference to real world problems using R and JAGS.

By Artem B

Aug 25, 2019

It is very concise, but informative course. It combines both theory and practice in R, which are easy to follow.

By Ian C

Jun 17, 2020

I really enjoyed the course! Thank you for the very interesting and thought-provoking lectures and assignments.

By Sharang T

Aug 16, 2020

It was a very informative course and it was very useful in giving an introduction to a whole new field for me

By Juan C

Jan 29, 2019

Muy recomendable para los investigadores y profesionales que quieren desarrollar productos y procesos nuevos.

By Ariel A

Aug 28, 2017

This course is a great start for everyone who wants to dive into Bayesian Statistics. Very clear and helpful.

By Hyun J K

Oct 13, 2019

Perfect combination of theory part + application part

Recommend to people who took the basic Bayesian class

By Stephen

Mar 17, 2019

Fairly good introduction to basic Bayesian statistical models and JAGS, the package to fit those models.

By Tomas F

Sep 19, 2021

I really, really enjoyed this course. So much so, that I'm definitively going to take the next one.

By Chow K M

Apr 20, 2021

The hands-on application with guidance helps one navigate between understanding and implementation.

By Cardy M I

Jan 29, 2019

This course helped me to get some experience at building Bayesian models and how they are applied.

By Nirajan B

Jan 17, 2021

Amazing course. Never taken a course of such an impressive level at coursera. Highly recommended.

By Madayan A

Sep 4, 2019

Very good course, a little bit to slow at some point but this is marginal in the overall feeling.

By Jaime A C

Jan 10, 2022

Excellent material, top quality instructor and very well designed course. I've learned a lot.

By Sariel H

Nov 29, 2020

Very comprehensive and practical. The course requires some experience with R programming.