Bayesian Data Analysis, The Basic Basics: Inference on a Binomial Proportion

For our slipped-to-early-October Meetup, we're thrilled to have Rob Mealey, of former host newBrandAnalytics, presenting the next in our occasional Data Science Classroom series! Bayesian data analysis is a critically important modern skill for flexibly modelling and understanding complex (or, as we'll see, simple) data.

New things:

New location! We're thrilled to be hosting this event in Google's gorgeous (and very colorful) meeting space! Thank you, Google! (And thank you newBrandAnalytics for your outstanding hosting in recent months!) New informal themed discussions! Before the main event, during the refreshments and mingling portion of the evening, we're going to set aside an area for people interested in a particular topic to meet each other and discuss that topic. This month, we've chosen Python data analysis as the theme. Agenda:

6:30pm -- Networking and Refreshments (Discussion theme: Python) 7:00pm -- Introduction 7:15pm -- Rob's presentation and Q&A around 8:30pm -- Adjourn for Data Drinks Abstract:

Compared with traditional statistical methods, the basic toolkit of Bayesian statistics produces more intuitive, easier to understand -- and use and update and compare -- outputs through comparatively difficult computational and mathematical procedures. Everything in and out of a Bayesian analysis is probability and can be combined or broken apart according to the rules of probability. But understanding code and sampling algorithms -- really understanding the algorithms and computation -- and a much deeper grasp of probability distribution theory are much more important in understanding Bayesian inference earlier on.

This tutorial is an introduction to the basic basics of Bayesian inference through a self-contained example involving data simulation and inference on a binomial proportion, such as vote share in a two-way election or a basketball player's free-throw percentage. Time allowing, we will also introduce some more advanced concepts. It is meant to help people with a general understanding of traditional statistics and probability take that first step towards drinking the Bayesian kool-aid. It is delicious stuff, I promise. Tastes like genuine reductions in uncertainty. And cherries.

Bio:

Rob Mealey has a BA in Political Science from St. Michael College and an MS in Applied Economics from Johns Hopkins. He works as a data scientist at DC-based social media analytics start-up newBrandAnalytics, surfacing disruptive and actionable findings from messy and disparate data sources for organizations and companies large and small across many different sectors. He previously worked at Fannie Mae... afterwards... where he single-handedly figured out what went wrong. It isn't his fault if no one listened.

Rob blogs at http://www.obscureanalytics.com and tweets at @robbymeals (http://twitter.com/robbymeals).