*** The class will meet Fridays 12:30-3:20 pm in room 309, South Hall, Psychology Building ***

In this course we will introduce some mathematical modeling approaches in psychology. We will first review some basic concepts of probability and random variables. We then introduce the concept of maximum likelihood, a model-fitting approach commonly used in mathematical psychology. Some Bayesian reasoning will be introduced along the way. In the second part of the course we will introduce several applications of mathematical modeling. Possible topics include threshold theories in signal detection, multinomial processing tree models (in clinical assessment), Choice models, Markov chains and random walk models, and Bayesian models, etc.

We will use R, a free software environment for statistical computing and graphics that can be downloaded from the web page http://www.r-project.org/ for some of the class presentation and some of the homework problems.

Problem sets and take-home group projects will be assigned from time to time. The take-home group projects include reading papers and preparing for class presentation, model fitting and data analysis using R, ... etc.

Course grades will be based on (1) the problem sets, (2) the take-home group projects, and (3) in-class participation and discussion.