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

4.8
stars
484 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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151 - 164 of 164 Reviews for Bayesian Statistics: Techniques and Models

By Eddie G

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Jan 21, 2021

Very comprehensive and challenging course. The explanations/rationale could be done better In the statistical programming parts.

By Daniele M

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Feb 11, 2020

Classes are very good, but people do not put much effort on peer review coments.

By Eric A S

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Jan 12, 2020

This course gives a very good introduction to Bayesian modeling in R using MCMC.

By Satish C S

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Oct 13, 2021

The course is very helpful for those who wanted to learn the Bayesian modeling.

By Dziem N

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Jun 22, 2020

The programming examples are excellent. Thank you...

By Stéphane M

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Feb 25, 2019

Good balance between courses and codes exercises

By SANDRA H M

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Jul 17, 2020

I think this course is hard.

By Vittorino M C

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Jul 31, 2020

I learn a lot, thank you.

By Jaan Ü

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May 31, 2024

Could use some chunking.

By Leon K

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Jan 28, 2022

High quality videos and lots of examples. Nonetheless, in some cases I felt that key concepts (e.g., Metropolis-Hastings algorithm) were only introduced on a technical/formulas level whithout giving an understanding why it works. From time to time, an overview on things would have been nice. E.g., when do I actually use which method. This is why I can only give 3/5.

By Juan J G T

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May 3, 2022

Te obliga a hacer una prueba final abierta, debiese ser opcional, entiendo sirve para la clasificación, pero si ya aprendiste los contenidos esenciales y no te interesa el curso para certificar ya, lo encuentro innecesario

By Brian M

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Jun 6, 2024

The course is good until the final Capstone which requires peer review. There are too few students to support peer reviewed assignments

By Serum N

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Feb 26, 2020

Such shallow course. You will be better off reading chapter1 of Bayesian data analysis. Don't waste your time here.

By Sujan D

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May 31, 2023

Why i still didinot receive the certificates???