Friday, 02 November 2012 at 11:30am
Room 1400 Biomedical and Physical Sciences Bldg.
Refreshments at 11:15
Department of Biochemistry and Molecular Biology,
University of California, Los Angeles
Title: Turning the Scientific Method into Math: Empirical Information Metrics for Experiment Planning
In this talk we outline some mathematical questions that emerge from trying to “turn the scientific method into math.” Specifically, we consider the problem of experiment planning (choosing the best experiment to do next) in explicit probabilistic and information theoretic terms. We formulate this as an information measurement problem; that is, we seek a rigorous definition of an information metric to measure the likely information yield of an experiment, such that maximizing the information metric will indeed reliably choose the best experiment to perform. We present the surprising result that defining the metric purely in terms of prediction power on *observable* variables yields a metric that can converge to the classical mutual information I(X;Omega) measuring how informative the experimental observation X is about an underlying *hidden* variable Omega. We show how the expectation potential information metric can compute the “information rate” of an experiment as well its total possible yield, and the information value of experimental controls. To illustrate the utility ofthese concepts for guiding fundamental scientific inquiry, we present an extensive case study (RoboMendel) applying these metrics to propose sequences of experiments for discovering the basic principles of genetics.