DSS-2019-02: MICHAEL BETANCOURT (STAN CORE)

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Commonwealth Bank of Australia

South Building 1 Harbour Street · Sydney

How to find us

Please go to Commonwealth Bank Place (South Building) in Darling Harbour.

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Details

Data Science Sydney proudly presents our speaker for February 2019:

MICHAEL BETANCOURT: SCALABLE BAYESIAN INFERENCE WITH HAMILTONIAN MONTE CARLO

200 seats available, first come - first served for members on the RSVP-yes list. Please ensure that you keep your RSVP up to date and make you spot available for others as soon as possible.

To comply with CBA Security we need your FIRST and LAST NAME before the event. If these are not your profile name, please enter them when you register. Members who do not provide first and last name will be removed from the guest list and will not be able to attend.

Registration opens at 5:30pm and close at 6:15pm, sharp. Food and beverages between 6pm and 6:15pm and late comers cannot be admitted.

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About the Talk: Despite the promise of big data, inferences are often limited not by sample size but rather by systematic effects. Only by carefully modeling these effects can we take full advantage of the data -- big data must be complemented with big models and the algorithms that can fit them. One such algorithm is Hamiltonian Monte Carlo, which exploits the inherent geometry of the posterior distribution to admit full Bayesian inference that scales to the complex models of practical interest. In this talk I will present a conceptual discussion of the challenges inherent to Bayesian computation and the foundations of why Hamiltonian Monte Carlo in uniquely suited to surmount them.

About the Speaker: Michael Betancourt is the principal research scientist with Symplectomorphic, LLC where he develops theoretical and methodological tools to support practical Bayesian inference. He is also a core developer of Stan, where he implements and tests these tools. In addition to hosting tutorials and workshops on Bayesian inference with Stan he also collaborates on analyses in epidemiology, pharmacology, and physics, amongst others. Before moving into statistics, Michael earned a B.S. from the California Institute of Technology and a Ph.D. from the Massachusetts Institute of Technology, both in physics.

Website: https://betanalpha.github.io
Twitter: @betanalpha