Friday, 02 November 2012 at 11:30am
Room 1400 Biomedical and Physical Sciences Bldg.
Refreshments at 11:15
Speaker:
Christopher Lee,
Department of Biochemistry and Molecular Biology,
University of California, Los Angeles
Title: Turning the Scientific Method into Math: Empirical Information Metrics for Experiment Planning
Abstract:
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.