Reinforcement Learning Methods in Public Health.

Purpose: Reinforcement learning (RL) is the subfield of machine learning focused on optimal sequential decision making under uncertainty. An optimal RL strategy maximizes cumulative utility by experimenting only if and when the information generated by experimentation is likely to outweigh associated short-term costs. RL represents a holistic approach to decision making that evaluates the impact of every action (ie, data collection, allocation of resources, and treatment assignment) in terms of short-term and long-term utility to stakeholders.

Likelihoods for fixed rank nomination networks.

Many studies that gather social network data use survey methods that lead to censored, missing, or otherwise incomplete information. For example, the popular fixed rank nomination (FRN) scheme, often used in studies of schools and businesses, asks study participants to nominate and rank at most a small number of contacts or friends, leaving the existence of other relations uncertain. However, most statistical models are formulated in terms of completely observed binary networks.