Abstract
The maximum entropy principle (maxent) has been applied to solve density estimation problems in physics (since 1871), statistics and information theory (since 1957), as well as machine learning (since 1993). According to this principle, we should represent available information as constraints and among all the distributions satisfying the constraints choose the one of maximum entropy. In this overview I will contrast various motivations of maxent with the main focus on applications in statistical inference. I will discuss the equivalence between robust Bayes, maximum entropy, and regularized maximum likelihood estimation, and the implications for principled statistical inference. Finally, I will describe how maxent has been applied to model natural languages and geographic distributions of species.
Bio
Venue, Date, and Time
Venue: NSH 1507
Date: Monday, November 19
Time: 12:00 noon