A webinar and online workshop for learning Bayesian analysis

September 13, 2018
By

(This article was first published on R – Dataviz – Stats – Bayes, and kindly contributed to R-bloggers)

I am running a couple of online events through BayesCamp next month that might interest you if you want to learn more about Bayesian analysis.

Firstly, the Bayesian Taster webinar. This lasts for one hour and costs only ten of those British pounds (currently, 13 USD or 11 EUR). There’s no maths and no coding; this is for complete beginners and is all about common sense. If you get the fundamental concepts right, you won’t get tripped up as it gets more complex later on. We will think about defining analytical problems in probability terms, what probability can be used for, and how practically to go about getting answers (fitting probability models to your data). We’ll look at a range of real-life problems with data and models that are too hard with old-fashioned stats / machine learning, but readily solved with Bayes. I’ll describe the spectrum of available software. This happens on 12 October, around lunch time for Africa and Europe, then later around lunch time for eastern Americas, or breakfast time for western Americas. You can book here (Afro-Euro edition) or here (Americas edition).

Secondly, a half-day online workshop called Packages for Bayesian Analysis in R, which is ideal for anyone with some R familiarity, who knows in essence what Bayesian analysis is about, but wants to find out about the options for actually doing it. We will look at a range of packages, from those that are easier to learn but restrict you to a collection of preset models, through to probabilistic programming that allows full flexibility. There will be plenty of mini exercises for you to try out on your own computers to get a feel for it as we go along. This will happen on 26 October, 1300–1700 UK time. You can book for this workshop here.

To leave a comment for the author, please follow the link and comment on their blog: R – Dataviz – Stats – Bayes.

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