It is a working seminar in various topics in statistics. Each semester and during the summer, we choose some topic of interest and meet once a week (usually on Friday afternoon an hour or so before happy hour) to discuss the topic.

Sometimes we work through a recent monograph on the chosen topic, and sometimes we experiment with one or more computer packages. Sometimes we do small Monte Carlo studies, which participants may follow up with larger MC studies. We usually have a laptop with a projector so everybody can see the programs and the output.

The idea is to have some fun and to learn something.

Students can register for the seminar for 1 hour of credit. Students who register will be required to make presentations. Grades are assigned based on these presentations and general participation in the seminar. Many of the attendees, however, are just faculty or students who want to learn something and who don't need the credit. On the other hand, students who want to do a significant amount of work in the topic of the seminar may be allowed to register for 3 hours of credit in CSI 779, or STAT 789, or a variable number of credits in CSI 796. Students registered for these courses meet with the seminar group and also individually with the instructor to work on an assigned research project.

**Anyone is welcome to attend.**

**
Spring, 2014: CSI 991, Section 001:
Recent articles in computational learning and computational statistics.
**

Time and place: Fridays from 4:00 to 5:00 in Music Theater Building 1008.

Sometimes we make web pages and sometimes we don't and
some are more complete than others.

Fall, 2013: CSI 991, Section 004:
Recent articles in computational learning and computational statistics.

Spring, 2013:
Evidence in statistical inference.

Fall, 2012:
Recent articles in computational learning and computational statistics.

Summer, 2012:
Recent articles in computational learning and computational statistics.

Spring, 2012:
Comparison of Bayesian and frequentist methods in statistics

Fall, 2011:
Bayesian Methods in Statistics

Spring, Summer, 2011:
Latent Variable Models

Fall, 2010:
Bayesian Computations in R

Spring, 2010:
Statistical Learning

Fall, 2009: Statistical Learning

Summer, 2009:
Statistical Learning

Spring, 2009:
Statistical Computing in R

Fall, 2008:
Statistical Computing in R

Spring, 2008:
Statistical Learning.

Fall, 2007:
Statistical Learning.

Spring, 2007:
Pattern classification

Fall, 2006:
Linear Models in R.

Summer, 2006:
R Graphics.

Spring, 2006:
Topics in the Exploration of Data.

Fall, 2005:
Topics in the Exploration of Data.

Spring/Summer, 2005:
Topics in the Exploration of Data.

Fall, 2004:
Topics in the Exploration of Data.

Spring/Summer, 2004:
Data Mining.

Fall, 2003:
Data Mining.

Summer, 2003:
Logistic Regression.

Spring, 2003: New (and Old) Ways of Looking at Regression
(Continuation of topics from Fall, 2002.)

Fall 02: New (and Old) Ways of Looking at Regression

Spring 02: Statistical Learning

Fall 01: Classification and Regression Trees; CART and MARS

Summer 01: More on Robust Statistical Methods

Spring 01: Robust Statistical Methods

Spring 96: Alternatives to Least Squares II

Fall 95: Alternatives to Least Squares

Spring 95: Fitting Generalized Additive Models

Send suggestions to jgentle@gmu.edu or to csutton@gmu.edu.