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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Bayesian Statistics: Techniques and Models
University of California, Santa CruzÜber diesen Kurs
Kompetenzen, die Sie erwerben
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University of California, Santa Cruz
UC Santa Cruz is an outstanding public research university with a deep commitment to undergraduate education. It’s a place that connects people and programs in unexpected ways while providing unparalleled opportunities for students to learn through hands-on experience.
Lehrplan - Was Sie in diesem Kurs lernen werden
Statistical modeling and Monte Carlo estimation
Statistical modeling, Bayesian modeling, Monte Carlo estimation
Markov chain Monte Carlo (MCMC)
Metropolis-Hastings, Gibbs sampling, assessing convergence
Common statistical models
Linear regression, ANOVA, logistic regression, multiple factor ANOVA
Count data and hierarchical modeling
Poisson regression, hierarchical modeling
Bewertungen
Top-Bewertungen von 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!!!
The course was really interesting and the codes were easy to follow. Although I did take the previous course for this series, I still found it hard to grasp the concepts immediately.
I learned a lot about MCMC. This course is taught using R, but I personally was also working on it in python at the same time. I would love to try a higher class. Thank you!
Excellent teacher and very well taught. Right amount of theory and programming combination. Made the subject easy to learn. Enjoyed it very much. Thank you very much.
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