that's like, so random! monte carlo for data science

That’s like, so random!Monte Carlo for Data Science

Corey Chivers, PhD@cjbayesian

Drawing samples is cool and all,

but what can I do with them?

1. Understand where obscure statistics come from

2. Make your own statistic!

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

3.Avoid having to do hard (and sometimes impossible)

math

4. Understand what inferences you can make with

your data

4. Propagate uncertainty in complex predictive models

See next talk!

4. Run ‘what if’ scenarios

What if we were to see a surge in patients in a given unit, how would this propagate to the rest of the hospital?

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.

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;

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:corey.chivers@uphs.upenn.edu @cjbayesian