I’d love to receive feedback on a project proposal for pymc3 for GSoC 2018. The idea came in a conversation with Thomas Wiecki last week about a summer project regarding cognitive neuroscience modeling using hierarchical Bayesian methods (and inspired by the existing GSoC project for ABC). This is the project abstract:
HDDM is a pymc2-based Python library for hierarchical Bayesian parameter estimation the Drift Diffusion Model, a model used in cognitive neuroscience model to study decision making. After conversations with Michael J. Frank and Thomas Wiecki, we came up with the following project proposition, intended to migrate HDDM to pymc3 to support its continued development and extend its capabilities. As I envision the process, if I document it sufficiently thoroughly, my work could also serve as a guide for domain experts in other (not statistics) disciplines to implement custom likelihoods and toolboxes for recurring problems in their fields. I then intend to begin researching and implementing ABC methods as part of pymc3, with an eye towards methods useful in the sort of problems encountered in cognitive neuroscience, such as hierarchical model fitting and regression between neural correlates and parameters.
Thank you very much,