This talk will describe a new approach for optimizing dynamic treatment regimes that bridges the gap between Bayesian inference and Q-learning. The proposed approach fits a series of Bayesian regression models, one for each stage, in reverse sequential order. Each model regresses the remaining payoff assuming optimal actions are taken at subsequ...
Creator:
Murray, Thomas (University of Minnesota, Twin Cities)
Created:
2018-11-07
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
A dynamic treatment regime is a sequence of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual’s own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential optimization problem ...
Creator:
Zhang, Min (University of Michigan)
Created:
2018-11-09
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
