StanCon 2019 Cambridge UK, August 20-23, 2019 at University of Cambridge, 
Cambridge UK.


Early Registration deadline 15 May! Registering early saves you money andmakes 
our planning easier.

**Website**

Full information is available at StanCon 2019 website
<https://mc-stan.org/events/stancon2019Cambridge/>

**Summary of the information**

What: Two days of tutorials and two days of talks, fancy dinner at Kings 
College, open discussions, and statistical modeling in beautiful Cambridge and 
one or more football (soccer) matches.

When: August 20-21 Tutorials, August 22-23 Conference, 2019

Where:

Tutorials: King’s College and Caius College.

Conference: West Road Concert Hall University of Cambridge 11 West Road, 
Cambridge CB3 9DP UK

**Tutorials**

- Introduction to Stan for the Statistically Literate. Jonah Sol Gabry, Lauren 
Kennedy, 2 days.

- Hierarchal Modeling with Stan. Ben Goodrich, 1 day.

- Population and ODE-based models using Stan and Torsten. Charles Margossian, 
Yi Zhang, 1 day.

- More coming.....

**Invited speakers**

- David Spiegelhalter, University of Cambridge: "Communicating Uncertainty 
about Facts, Numbers and Science"
- Lauren Kennedy, Columbia University: "Out of sample prediction and the quest 
for generalization"


**Contributed talks**

The contributed talk deadline was April 30, 2019.

**Contributed posters**

We will accept poster submissions on a rolling basis until August 15, 2019. See 
the conference web page for submission instructions.

**Sponsors**

- Generable <https://generable.com>
- Astra Zeneca <https://www.astrazeneca.com/>

If you’re interested in sponsoring StanCon 2019 Cambridge, pleasereach out to 
[log in to unmask] Your generous contributions will ensure that our 
registration costs are kept as low as possible and allow for us to subsidize 
attendance for students who would otherwise be unable to come as well as 
supporting Stan.

**Stan**

Stan (http://mc-stan.org) is a statistical modeling language used by thousands 
of scientists, engineers, and other researchers for statistical modeling,data 
analysis, and prediction. It is being applied academically and commercially 
across fields as diverse as ecology, pharmacometrics, physics, political 
science, finance and econometrics, professional sports, real estate, 
publishing, recommender systems, and educational testing.

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