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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Bayessche Statistik
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Spezialisierung Bayessche StatistikUniversity of California, Santa CruzÜber diesen Kurs
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- Gibbs Sampling
- Bayesian Statistics
- Bayesian Inference
- R Programming
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Lehrplan - Was Sie in diesem Kurs lernen werden
Statistical modeling and Monte Carlo estimation
Markov chain Monte Carlo (MCMC)
Common statistical models
Count data and hierarchical modeling
Bewertungen
- 5 stars83,14 %
- 4 stars12,86 %
- 3 stars2,21 %
- 2 stars0,88 %
- 1 star0,88 %
Top-Bewertungen von BAYESIAN STATISTICS: TECHNIQUES AND MODELS
Very comprehensive and challenging course. The explanations/rationale could be done better In the statistical programming parts.
Very good course giving a good practical kickoff to a very interesting and exciting topic of Bayesian statistics.
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.
Outstanding, Excellent, Must do for statistician. I'm from Civil Engg Background easily capable to learn the course
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