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Bayesian Modeling, Inference, Prediction, and Decision-Making – 10-day course


Friday, January 11, 2013 - 9:00am - 5:00pm


eBay North Campus
2211 1st St.
San Jose, CA 95131
Professor David Draper, UC Santa Cruz

Event Details

Sponsor and Organizer: eBay

Co-organizers: San Francisco Chapter of American Statistical Association (SFASA) and San Francisco Chapter of ACM

The course will meet on ten successive Fridays from 9-5 pm starting Jan 11, 2013. 
Attendance required at all 10 sessions: Fridays 1/11, 1/18, 1/25, 2/1, 2/8, 2/15, 2/22, 3/1, 3/8, 3/15. 
Uncertainty -- a state of incomplete information about something of interest to you -- is pervasive in almost everything people do. 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. In this course, for one full day per week over a ten-week span, I will provide a 60-hour introduction to the Bayesian paradigm, based on a series of real-world case studies and featuring a wealth of computational detail in the statistical freeware environments R and WinBUGS. Topics will include prior, likelihood, posterior and predictive distributions, maximization of expected utility, conjugate and non-conjugate analysis, Markov-chain Monte Carlo computational methods, one-parameter and multi-parameter problems, and hierarchical and mixture modeling; in addition to introductory topics, in 10 one-day sessions we'll be able to explore a variety of intermediate- and advanced-level ideas,  including optimal Bayesian model specification and Bayesian nonparametric methods. 
Cost: $300
Availability: Only 15 seats available from SFBayACM.
Registration deadline: Registration will close on Jan. 3nd, 2013

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Tags: ACM, Bayesian, SFBay, course, decision, development, eBay, inference, making, modeling, More…professional, statistics, ucsc


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