Abstract: This is a 3-hour workshop on Stan (https://mc-stan.org). The overall goal of the workshop will be to make the best use of time to answer as many Stan-related questions as possible. The level of the workshop will be intermediate to advanced, but anyone is welcome to join. The workshop will be taught by Daniel Lee. Daniel is one of the original Stan developers (started in 2011). He's been involved in the whole stack: language, CmdStan, RStan, PyStan, continuous integration, setting up the forums, StanCon, and more. He's had a lot of experience with debugging computational issues, the crossover between statistical models and computational, understanding how all the pieces fit in together, and knowing a lot of different ways to accomplish the same thing in the Stan language.The format of this workshop won't be a straight online lecture. I'm personally tired of Zoom meetings; I don't think the intro course I teach works in this format. Instead, we'll have a blend of an instructor-led example, a masterclass, and an AMA. Please come with questions or ask them as we go along. Here's a rough plan (but we can deviate from this):1. Brief introduction to Stan. Goal: understand what Stan is, what the inferences are (and why they're different), and agree on terminology.2. Walkthrough an example (survival or PK/PD model). Show differences between posterior distributions and point estimates. Maybe discuss quality of MCMC sampling.3. One-on-one with a participant. Walk through their problem, attempt at a solution, walk through the different modeling choices we're making, how to structure simulated data, etc.4. One-on-one with another participant.5. Questions / Wrap up.
Speaker: Daniel Lee
Affiliation: Generable
Title: Stan
Abstract_Sanitzed: This is a 3-hour workshop on Stan (https//mc-stan.org). The overall goal of the workshop will be to make the best use of time to answer as many Stan-related questions as possible. The level of the workshop will be intermediate to advanced, but anyone is welcome to join. The workshop will be taught by Daniel Lee. Daniel is one of the original Stan developers (started in 2011). He's been involved in the whole stack language, CmdStan, RStan, PyStan, continuous integration, setting up the forums, StanCon, and more. He's had a lot of experience with debugging computational issues, the crossover between statistical models and computational, understanding how all the pieces fit in together, and knowing a lot of different ways to accomplish the same thing in the Stan language.The format of this workshop won't be a straight online lecture. I'm personally tired of Zoom meetings; I don't think the intro course I teach works in this format. Instead, we'll have a blend of an instructor-led example, a masterclass, and an AMA. Please come with questions or ask them as we go along. Here's a rough plan (but we can deviate from this)1. Brief introduction to Stan. Goal understand what Stan is, what the inferences are (and why they're different), and agree on terminology.2. Walkthrough an example (survival or PK/PD model). Show differences between posterior distributions and point estimates. Maybe discuss quality of MCMC sampling.3. One-on-one with a participant. Walk through their problem, attempt at a solution, walk through the different modeling choices we're making, how to structure simulated data, etc.4. One-on-one with another participant.5. Questions / Wrap up.