That's like, so random! Monte Carlo for Data Science
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That’s like, so random!Monte Carlo for Data Science
Corey Chivers, PhD@cjbayesian
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Drawing samples is cool and all,
but what can I do with them?
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1. Understand where obscure statistics come from
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2. Make your own statistic!
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Chivers, C., Leung, B., Yan, N. D. (2014), Validation and calibration of probabilistic predictions in ecology. Methods in Ecology and Evolution, 5: 1023–1032. doi: 10.1111/2041-210X.12238
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3.Avoid having to do hard (and sometimes impossible)
math
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4. Understand what inferences you can make with
your data
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4. Propagate uncertainty in complex predictive models
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See next talk!
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4. Run ‘what if’ scenarios
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What if we were to see a surge in patients in a given unit, how would this propagate to the rest of the hospital?
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sim·u·di·dactic adj.*/ˈsimyəˌdīdakt/
To understand by creating a representation or model of real-world phenomena. Particularly, using randomization and computation to understand complex systems and processes.
C2013: From Latin simulre, simult-, from similis, like and Greek didaktos, taught;
* I totally just made this up, but it could be a thing.
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sim·u·di·dactic adj.*/ˈsimyəˌdīdakt/
To understand by creating a representation or model of real-world phenomena. Particularly, using in randomization and computation to understand complex systems and processes.
C2013: From Latin simulre, simult-, from similis, like and Greek didaktos, taught;
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Data Science
Seeking Software Engineers (Sr. & mid-level) to help us build out our real-time predictive application platform
http://www.med.upenn.edu/predictivehealthcare/
• Develop data products and predictive applications • Collaborate with top medical professionals• Revolutionize Health care delivery
Contact:[email protected] @cjbayesian