| Session | ||
ME1 - AI10: Bayesian method and machine learning application
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| Presentations | ||
Diversified learning: Bayesian control with multiple biased information sources 1NUS Business School, National University of Singapore; 2Sauder School of Business, University of British Columbia We consider a decision-maker (DM) who can sample from multiple information sources to learn a state before making an earning decision. The DM optimizes his sampling and earning decisions to maximize his payoffs. The problem is motivated by financial and healthcare applications with multiple information sources. We develop a Bayesian control framework for this problem and solve it in the estimation and testing settings. We also develop an efficient algorithm for the general control setting. Strategic choices and routing within service networks: modeling and estimation using machine learning The Wharton School, University of Pennsylvania TBD | ||