The virtual course "Bayesian Statistics: Techniques and Models - Virtual Course - Coursera", is a course with different contents and that offers video classes of Approx. 30 hours to complete. Explore its essential features, and click the orange button to get detailed information on the Coursera e-Learning platform
This is the second in a two-course sequence that introduces the fundamentals of Bayesian statistics.
It is based on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through the use of simple conjugate models.
Real-world data often requires 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 the Markov Chain Monte Carlo (MCMC) methods, which allow the sampling of posterior distributions that have no analytical solution.
We will use the freely available and open source software R (some experience is assumed, eg completing the above course in R) and JAGS (no experience required).
We will learn to build, fit, evaluate, and compare Bayesian statistical models to answer scientific questions involving continuous, binary, and counting data.
This course combines video lectures, computer demonstrations, readings, exercises, and discussion forums to create an active learning experience.
The lectures provide some basic mathematical development, explanations of the statistical modeling process, and some basic modeling techniques commonly used by statisticians.
Computer demos provide concrete, hands-on tutorials.
Completion of this course will give you access to a wide range of customizable Bayesian analytical tools for your data.
The lectures provide some basic mathematical development, explanations of the statistical modeling process, and some basic modeling techniques commonly used by statisticians.
Computer demos provide concrete, hands-on tutorials.
Completion of this course will give you access to a wide range of customizable Bayesian analytical tools for your data.
The lectures provide some basic mathematical development, explanations of the statistical modeling process, and some basic modeling techniques commonly used by statisticians.
Computer demos provide concrete, hands-on tutorials.
Completion of this course will give you access to a wide range of customizable Bayesian analytical tools for your data.
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University of California at Santa Cruz
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