Maximum+Likelihood+Estimation

Maximum Likelihood Estimation (MLE) (Goodness-of-Fit) (GWU EMSE-271)
Index | Topics (Logical Lectures) | Lectures | Problems | Readings | References | Concepts


 * Quick Answer(s):** (EMSE 280, Spring 2009)
 * 1) The likelihood is found by maximizing the likelihood function. EMSE 280 spring 2009, Lecture 8 Slide 5
 * 2) "For a fixed set of data and underlying probability model, maximum likelihood picks the values of the model parameters that make the data "more likely" than any other values of the parameters would make them." - [|Wikipedia]
 * 3) Likelihood: "The probability of an event based on current observations." - [|Electronic Statistics Textbook]

EMSE 271 slides 90-94 show an example of how to define a distirbution and perform a partial differentiation to obtain the Maximum Likelihood Estimation (MLE). Note: Homework 3 does not require computing an MLE using partial differentiation.

- EMSE 271, Fall 2009 (Slide 95)


 * Sources:**
 * EMSE 271, Fall 2009 (Slides 90-94)
 * EMSE 280, Spring 2009