STAT 625: Spatial Statistics and Image Analysis
Class meets: M 10:30 am -- 1:00 pm in:
(see note at end of page)
Instructor: Ranjan Maitra
Office: Mathematics and Psychology, (MP) 436
Office Hours: to be announced
Systems Administrator: Boris Alemi
Office: Mathematics and Psychology, (MP) 413
Computer accounts on math.umbc.edu may be obtained by filling in
the form posted outside Mr. Alemi's door.
Projects and Presentation: 50%
Please send me e-mail
if you intend to take the course so that I may have an idea of the
approximate class size. Also, it may be possible to move up the time to
earlier in the day in case you cannot make it at this time. But, please
do let me know!
This is a special topics course concerned with the statistical
analysis of geo-referenced or spatial data. The textbook for the class
is: Brian Ripley's Spatial Statistics, published
This course is primarily applications-oriented
and will, as such, revolve around the applications of spatial statistics
in fields such as geology, environmental monitoring, and image analysis.
The broad outline for this course is as follows:
Introduction to Spatial Statistics through Applications
Lattice Models: Markov Chains and Markov Random Fields. The use
of Markov Random Field priors in Bayesian Inference. Markov Chain Monte
Carlo Methods. Applications to Spatial Models and Image Analysis
Statistical Image Analysis: Image Reconstruction, Image segmentation,
Mixture Analysis, Variance Estimation in tomographic images, Functional
imaging. Application to Magnetic Resonance Imaging, Transmission and Emission
Tomography, Astronomical modeling
Some topics in Spatial Point Process theory
Geostatistical Data: The Variogram and the Covariogram,
Prediction and Kriging, Isotropic Variograms, Space-time
Data, Anisotropic Variogram estimation
Homeworks will be handed out weekly, or bi-weekly. This will mostly
consist of applying and exploring the concepts learnt in class.
A considerable part of the homework will involve computer work.
There will be one project assigned to each person during the semester.
This project will involve either doing some exploratory work on a
research problem, including background literature study, etc, or
the statistical analysis of some appropriate dataset that you are interested
in. You are welcome to provide this dataset in consultation with me.
The course homepage will be located on the WorldWide Web at:
I will try and keep this homepage as upto date as possible. However,
you are still responsible for any announcements made in class.
Stat 451 or equivalent, and at least concurrent registration
in Stat 452 and Stat 453 or equivalent.
Knowledge of one programming language like Fortran or C.