1. Intro:
Vivian will cover the following topic using R:
• write short scripts to define a Bayesian model
• use or write functions to summarize a posterior distribution
• use functions to simulate from the posterior distribution
• construct graphs to illustrate the posterior inference
And she will show a few real world applications of Bayesian modeling.
2. Speaker:
Vivian Zhang, CTO of SupStat Inc which is a data analytic consulting firm.
3. Perquisites:
Basic R programming background.
4. Scenario Review:
Explanation will be provided subsequent sections.
5. Relevant Materials:
1) Meetup slides:
2) Meetup video:
3) ACF plot reading: https://sfb649.wiwi.hu-berlin.de...
4) Metropolis Hastings algorithm: https://en.wikipedia.org/wiki/Metropolis_Hastings_algorithm
5) Gibbs Sampling: https://en.wikipedia.org/wiki/Gibbs_sampling
6) https://en.wikipedia.org/wiki/Pythagorean_theorem
7) https://en.wikipedia.org/wiki/Harold_Jeffreys
8) https://en.wikipedia.org/wiki/Naive_Bayes_classifier
9) https://en.wikipedia.org/wiki/Thomas_Bayes
10) NaiveBayes from e1091 packakge: https://cran.r-project.org/web/packages/e1071/e1071.pdf
11) event link: https://www.meetup.com/NYC-Open-Data/events/209879402/
6. Further Information:
Twitter: @Vivian__Zhang @SupStat @NycDataSci
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