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.
Este curso forma parte de Programa especializado: Estadística bayesiana
ofrecido por
Programa especializado: Estadística bayesianaUniversidad de California en Santa CruzAcerca de este Curso
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Prueba Coursera para negociosHabilidades que obtendrás
- Gibbs Sampling
- Bayesian Statistics
- Bayesian Inference
- R Programming
¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?
Prueba Coursera para negocios
ofrecido por
Programa - Qué aprenderás en este curso
Statistical modeling and Monte Carlo estimation
Markov chain Monte Carlo (MCMC)
Common statistical models
Count data and hierarchical modeling
Reseñas
- 5 stars83,07 %
- 4 stars12,91 %
- 3 stars2,22 %
- 2 stars0,89 %
- 1 star0,89 %
Principales reseñas sobre BAYESIAN STATISTICS: TECHNIQUES AND MODELS
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!!!
Excellent course for introducing yourself to Monte Carlo Methods applied to Bayesian statistics. Highly recommended!
Very interesting.
I would like to have a follow on since the possible applications of the topics explained in the course.
Outstanding, Excellent, Must do for statistician. I'm from Civil Engg Background easily capable to learn the course
Acerca de Programa especializado: Estadística bayesiana
Preguntas Frecuentes
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