Estimating ODEs - Hands-on tutorial using Stan with Daniel Lee

This will be a hands-on tutorial using Stan!

We'll cover:

• how an ordinary differential equation (ODE) works within a statistical model
• how to specify it in Stan
• how to estimate the ODE parameters from Stan
• actually fit some data!

Please come with either: R and CmdStanR, Python and CmdStanPy, or CmdStan installed. If you're not familiar with any, please install R and CmdStanR. I'll provide magic invocations so you can follow along.

Daniel Lee is a computational Bayesian statistician who helped create and develop Stan, the open-source statistical modeling language. He has 20 years of experience in numeric computation and software; over 10 years of experience creating and working with Stan; and has spent the last 5 years working on pharma-related models including joint models for estimating oncology treatment efficacy and PK/PD models. Past projects have covered estimating vote share for state and national elections; clinical trials for rare diseases and non-small-cell lung cancer; satellite control software for television and government; retail price sensitivity; data fusion for U.S. Navy applications; sabermetrics for an MLB team; and assessing “clutch” moments in NFL footage. Daniel has led workshops and given talks in applied statistics and Stan at Columbia University, MIT, Penn State, UC Irvine, UCLA, University of Washington, Vanderbilt University, Amazon, Climate Corp, Swiss Statistical Society, IBM AI Systems Day, R/Pharma, StanCon, PAGANZ, ISBA, PROBPROG, and NeurIPS. He holds a B.S. in Mathematics with Computer Science from MIT, and a Master of Advanced Studies in Statistics from Cambridge University.