BARUG October Meeting
Agenda: 6:30 PM Pizza and networking 7:00 PM John Mount: Using R and Stan to Learn Preferences 7:30 PM Grail Speaker TBD 8:00 PM Bob Horton: Searching for concepts in semantic space 8:30 General Discussion 8:45 PM wrap up
Using R and Stan to Learn Preferences Abstract: A classic problem in online marketing is learning user preference from past user selections and rejections. This is a hard problem as most of the user’s behavior is hidden or unobserved and also confounded with nuisance issues such as competing offers and even order of presentation. Bayesian systems, such as Stan’s Markov chain Monte Carlo sampler are ideal for guessing at or inferring such hidden state. I will describe what Stan is, and show how to call Stan from R or Python to characterize and solve the problem. The strength of the method is: Stan lets the user concentrate on describing the problem, and keeps how the problem is solved somewhat separate from the specification. This makes Stan an excellent prototyping tool. I will share a few “tricks in thinking probabilistically” and how to run Stan and diagnose the results. Seeing how this is done should allow participants to try Stan on their own problems.
Speaker: Dr. John Mount is a data science developer, consultant, speaker, and trainer. He has a Ph.D in computer science from Carnegie Mellon. He is a general partner at Win Vector LLC and one of the authors of Practical Data Science with R (now in its second edition). He is also the co-author of numerous R packages, including vtreat which performs automatic reliable low-dimensional re-encoding of categorical variables for machine learning problems. Win Vector LLC would love to help you with your data science projects!
