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.
Этот курс входит в специализацию ''Специализация Байесовская статистика'
от партнера
Специализация Байесовская статистикаКалифорнийский университет в Санта-КрузеОб этом курсе
Будет ли вашей компании выгодно обучить сотрудников востребованным навыкам?
Попробуйте Coursera для бизнесаПриобретаемые навыки
- Gibbs Sampling
- Bayesian Statistics
- Bayesian Inference
- R Programming
Будет ли вашей компании выгодно обучить сотрудников востребованным навыкам?
Попробуйте Coursera для бизнеса
от партнера
Программа курса: что вы изучите
Statistical modeling and Monte Carlo estimation
Markov chain Monte Carlo (MCMC)
Common statistical models
Count data and hierarchical modeling
Рецензии
- 5 stars83,07 %
- 4 stars12,91 %
- 3 stars2,22 %
- 2 stars0,89 %
- 1 star0,89 %
Лучшие отзывы о курсе BAYESIAN STATISTICS: TECHNIQUES AND MODELS
Fantastic course that I was able to immediately incorporate into my work. Great mix of theory and hands on coding!
It is very concise, but informative course. It combines both theory and practice in R, which are easy to follow.
terrific, so I've learn quite a lot basic knowledge about MCMC. So I can build kinds of models with better understanding.
One of the best practical math courses present in coursera. Loved the course and will surely look upto the next course eagerly.
Специализация Байесовская статистика: общие сведения
Часто задаваемые вопросы
Когда я получу доступ к лекциям и заданиям?
Что я получу, оформив подписку на специализацию?
Можно ли получить финансовую помощь?
Остались вопросы? Посетите Центр поддержки учащихся.
