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Register for a Free Live Webcast of Bayesian Modeling & Python Data Tools Tues. March 19 @6:15pm MST

Event Details

Register for a Free Live Webcast of Bayesian Modeling & Python Data Tools Tues. March 19 @6:15pm MST

Time: March 19, 2013 from 6:15pm to 8:30pm
Location: University of Colorado Denver
Street: 1200 Larimer St North Classroom Building #1539
City/Town: Denver
Website or Map: http://www.meetup.com/Data-Sc…
Event Type: free, and, open, to, all
Organized By: Michael Walker - Data Science Group
Latest Activity: Mar 19, 2013

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Event Description

Register for a Free Live Webcast of Bayesian Modeling & Python Data Tools Tues. March 19 @6:15pm MST - 

Register @ http://bit.ly/135FxOK

For folks unable to attend in-person, register to attend the event and two (2) hours before the event we will email you a link to watch the event via live webcast.

 

University of Colorado Denver - Tuesday March 19, 2013 @ 6:00pm MST
Large auditorium (170 person capacity) with 20' screen.

Location: CU Denver - North Classroom #1539 - 1200 Larimer Street
Denver, CO 80217-3364 - Map: http://bit.ly/Tyznzg

Agenda:

6:00 - 6:15 Schmooze - Old Chicago Pizza will be served.

6:15 - 7:15 Bayesian Modeling by Mark Labovitz

7:15 - 8:30 Python Data Tools by Cary Miller

8:30 - 9:30 Network at Old Chicago at 14th and Market.
See: http://www.oldchicago.com/denver-market-street

Bayesian Modeling, Inference, Prediction, and Decision-Making - Abstract

Uncertainty -- a state of incomplete information -- is pervasive yet we often must make key decisions based on imperfect information. The Bayesian statistical approach to uncertainty quantification, which involves combining information, both internal and external to your available data sources, into an overall information summary, is both logically internally consistent and simple to describe: there's one equation for inference (drawing valid conclusions about the underlying data-generating process), one for prediction of observables, and one for optimal decision-making.

However, specifying the ingredients that, when combined, formulate a good model for your uncertainty is a process -- combining elements of both art and science, intuition and rigor -- that can take a lifetime to master.

Mark Labovitz, an independent data scientist, will provide a brief overview of Bayesian Probability Theory and important technologies. Mr. Labovitz has over thirty (30) years experience as a Statistical / Quantitative Analyst and Team Lead specializing in the quantitative analysis of financial and marketing data. He has an MBA from University of Pennsylvania, The Wharton School and two (2) PhD's: Geomathematics – The Pennsylvania State University; and Applied Mathematics (2004-2009), Concentration in Statistics– University of Colorado Denver.

Register @ http://bit.ly/135FxOK

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