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Bayesian Statistics

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.

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Rating 4.7 based on 59 ratings
Length 6 weeks
Effort 5 weeks of study, 4-6 hours/week.
Starts Oct 12 (last week)
Cost $49
From University of California, Santa Cruz via Coursera
Instructor Matthew Heiner
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Math And Logic Probability And Statistics

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What people are saying

bayesian statistics

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

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

This course fills an essential gap in learning Bayesian statistics, and provides concrete assistance in moving from theory to actual model writing in R and jags.

However, the course requires a fairly high level of comfort with both general Bayesian statistics and the R language.

great course This course is a perfect continuation of the Bayesian Statistics course by Prof. Herbert Lee.

Excellent for the beginners to the Bayesian Statistics as it allows to start confidently using Bayesian models in practice.

This course follows "Bayesian Statistics: From Concept to Data Analysis".

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very helpful

The course requires good understanding of Bayesian methods and linear modelling, something that is covered in previous course of this track from University of California Santa Cruz.All quizes are quite easy to complete after watching the videos, but don't be fooled by this apparent simplicity - there is much more to the class than just that.Capstone project is challenging and does put to test all of the topic discussed in class,discussion forums are very helpful and also are extremely interesting to read.I can strongly recommend this class to anyone who is interested in Bayesian Methods.I've seen quite a few of similar classes on Coursera, but this one is the best, in my opinion, but also is the hardest one.Do not miss out on Honors track, recommended supplementary reading and Capstone - those are the gems.

Very helpful!

Prior knowledge of the use of R can be very helpful.

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bayesian models

While managing to cover everything from the basics of MCMC through to the use of a number of different bayesian models.

The applied learning is supported by lessons in Bayesian theory, however, most of the learning is focussed on fitting, assessing and interpreting Bayesian models using rjags and the rjags language.

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well organized

This covered a large amount of material, but it was well organized, with a good number of problems to solve.

Great materials and well organized lecture structure.

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very interesting

The material is very very interesting, the videos are of high quality and the quizzes and project really helps you getting it together.

I found this course very interesting and informative.

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Rating 4.7 based on 59 ratings
Length 6 weeks
Effort 5 weeks of study, 4-6 hours/week.
Starts Oct 12 (last week)
Cost $49
From University of California, Santa Cruz via Coursera
Instructor Matthew Heiner
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Math And Logic Probability And Statistics

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