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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Habilidades que obtendrás
- 5 stars83,07 %
- 4 stars12,91 %
- 3 stars2,22 %
- 2 stars0,89 %
- 1 star0,89 %
Principales reseñas sobre BAYESIAN STATISTICS: TECHNIQUES AND MODELS
It is very concise, but informative course. It combines both theory and practice in R, which are easy to follow.
Great course. The instructor provided detailed code examples and clear explanations for model intuitions. The final capstone project is a plus.
One of the best practical math courses present in coursera. Loved the course and will surely look upto the next course eagerly.
Excellent course for introducing yourself to Monte Carlo Methods applied to Bayesian statistics. Highly recommended!
Acerca de Programa especializado: Estadística bayesiana
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