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:

  1. 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!)
  2. 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.

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  • Robert M.

    Hi all. Really appreciate everyone coming. Sounds like I rushed through things way too fast for some people. Sorry! I was worried I would do just that. I was trying to not talk down to all the massive brains in the room, but I should have been more careful. An earlier version of the same ideas can be found here: http://www.obscureanalytics.com­..., and playing with the code up on github can help. The books I've been using to learn this stuff are:
    Introduction to Bayesian Statistics, William Bolstad
    Doing Bayesian Data Analysis, John K Kruschke
    Scott Lynch's Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

    A good very basic intro book is:
    Introduction to Bayesian Statistics, William Bolstad

    I hope that some people got at least something out of it, and I really appreciated the opportunity to talk with you all!

    October 2, 2012

    • Ross M.

      Hey Rob,

      October 17, 2012

    • Ross M.

      Hey Rob, let's try this time with content <g> So, thinking of your Google/RCP Naive Bayes political poll analysis, I ran across another Tonka Toy(tm) that might be great for Bayesian analysis: http://en.wikipedia.o...­

      October 17, 2012

  • Robinson P.

    Very informative and well organized

    October 3, 2012

  • Aaron M

    Great presentation. Lots of new material and code for me the discuss, discover, and play with.

    October 2, 2012

  • Robert M.

    Here's a link to the slides:
    http://obscureanalytics.com/dsd...­

    October 2, 2012

  • Wesley C.

    I thought the pacing was excellent (for myself at least). Greatly enjoyed the talk.

    October 2, 2012

  • Jerzy W.

    Python users might also want to check out Think Stats by Allen Downey (free online, or paperback from O'Reilly). It's an intro stats book that takes the Bayes approach from the start, and it's targeted at coders.
    http://www.greenteapress.com/th...­

    October 2, 2012

  • A former member
    A former member

    Are the slides online anywhere? I found the code (https://github.com/robbymeals­) but couldn't find the presentation.

    October 2, 2012

    • Robert M.

      Hey Portman, just put the html for the slides up in a repo there, and will get them hosted on my site shortly. Thanks!

      October 2, 2012

  • Manuel O.

    Not being a data scientist but rather a grunt coder, I don't know if the point of the talk went past me. Rob rushed through the equations and code, so much that even if you knew what the equations and code were (I have some basic understanding of bayesian analysis, having written some stuff last year, and studied maths at university), you couldn't even scan them while they were on screen. That said, Rob knew his stuff, but so had the audience in order to follow the talk. But if you knew what was discussed, what was the message of the talk in itself? Was it about presenting bayesian analysis in comparison to more "conventional statistics". I hope I'm not being too critical, and I may just have completely missed, as I was a bit struggling by mathworlding some of the concepts used.

    October 2, 2012

  • A former member
    A former member

    After a 12-hour day I didn't have the mental capacity to keep up. But from what I absorbed, it seemed good.

    October 2, 2012

  • Dotty

    too fast-paced to really get anything useful from it.

    October 2, 2012

  • Vlad K.

    Great

    October 1, 2012

  • Jerzy W.

    Rob clearly knew his stuff, but rushed past both the equations and the code necessary to understand and implement these methods.

    October 1, 2012

  • Ross M.

    Thanks to Harlan and Rob for knocking it out of the park.

    October 1, 2012

  • Ross M.

    Excellent talk by Rob Mealey tonight. Superb pacing and content. Data Science DC bar is set pretty damn high now.

    October 1, 2012

  • jesse

    I look forward to understanding the algorithmic subtleties involved so that I don't screw up any of my stat/analyst co-workers work when playing with any of their predictive models or butchering their great R graphs in d3

    September 25, 2012

  • Jay K.

    A recent Reuters' poll on the Presidential election reported the credibility interval instead of the margin of error. This is the first time I've seen that. Go Bayes!

    September 20, 2012

  • lee de c.

    wasn't there recently a workshop at GMU on bayesiology?

    September 7, 2012

  • Sean S.

    For those that don't prefer to end their analytical evenings in a bar, I will be leading a migration to a nearby coffeeshop for after-meeting Data Coffee and chat.

    September 7, 2012

    • Ross M.

      How about a coffee..and a drink...and a coffee...and a drink....that's a zero mu, but a killer sigma.

      2 · September 7, 2012

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