In this paper, I provide a tutorial exposition on maximum likelihood estimation (MLE). The intended audience of this tutorial are researchers who practice mathematical modeling of cognition but are unfamiliar with the estimation method. Unlike least-squares estimation which is primarily a descriptive tool, MLE is a preferred method of parameter estimation in statistics and is an indispensable tool for many statistical modeling techniques, in particular in non-linear modeling with non-normal data. The purpose of this paper is to provide a good conceptual explanation of the method with illustrative examples so the reader can have a grasp of some of the basic principles
Dept. of Electrical Computer Engineering OOCL Transportation Co Dept, of Economics University of Ilinois at Urbana-Champaign 300 Central Ave Urbana IL 6180 University of Illinois at Urbana-Champaign Mountain view, CA 94043 Champaign, IL 61820 Abstract We provide a brief review of the EWPP operation [1].The Pool dispatcher is charged with determining on a daily basis We formulate a general framework of a competitive electric- the schedule for the so-called availability declaration period ity generation supply market(CEM, embodying the salient (ADP),a 39-bour