Chevron Left
Вернуться к Bayesian Statistics: Techniques and Models

Bayesian Statistics: Techniques and Models, University of California, Santa Cruz

4.8
Оценки: 153
Рецензии: 41

Об этом курсе

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....
Фильтр по:

Рецензии: 40

автор: Wangtx

Dec 11, 2018

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

автор: Arnaud Dion

Dec 08, 2018

Really interesting course. The coding session are useful and can be use cases for lots of various situations.

автор: Ahmed Mukhtar

Nov 12, 2018

If you want to become good in modelling it is recommended to enrol.

автор: Dongliang Yi

Sep 30, 2018

Great class.

автор: Ilia Selitcer

Sep 24, 2018

I found this course very interesting and informative.

автор: Nicholas William Tomasino

Sep 06, 2018

Very thorough instruction. Excellent feedback and support on forums.

автор: Hsiaoyi Hung

Jul 31, 2018

Great course to learn both theories and techniques!

автор: Victor Zaytsev

Jul 30, 2018

A very good practical and theoretical course

автор: Benjamin Osafo Agyare

Jul 08, 2018

This is a great course for an introduction to Bayesian Statistics class. Prior knowledge of the use of R can be very helpful. Thanks for such a wonderful course!!!

автор: Hugo Ricardo Correia Rodrigues

Jun 19, 2018

Brilliant course! Very well organized and with useful study cases.Suggestion: It would be nice to have the same examples in Python using, e.g. Stan or PyMC.