Imaging with Incomplete Information
Download as PDF
Course Description
Statistical and computational methods for inferring images from incomplete data. Bayesian inference methods are used to combine data and quantify uncertainty in the estimate. Fast linear algebra tools are used to solve problems with many pixels and many observations. Applications from several fields but mainly in earth sciences. Prerequisites: Linear algebra and probability theory.
Cross Listed Courses
Grading Basis
ROP - Letter or Credit/No Credit
Min
3
Max
4
Course Repeatable for Degree Credit?
No
Course Component
Lecture
Enrollment Optional?
No
Does this course satisfy the University Language Requirement?
No
Programs
CME262
is a
completion requirement
for:
- (from the following course set: )
- (from the following course set: )