Statistical Methods in Biomedical Informatics

Course Description

Recent advances in high-throughput experimental technologies generate enormous amounts of data. In order to extract insights from such large-scale data sets, robust statistical models and efficient computation methods are indispensable. This course introduces probability and statistical modeling and analytical methods commonly used in biomedical-informatics. Basic probability theory will be briefly reviewed and the course will focus on the construction and solving of statistical modeling based on real biomedical data sets. The methods covered include maximum likelihood estimation, Bayesian inference, dynamic programming, Markov Models, Monte Carlo simulation, classification and clustering. Students will learn to use statistical programs and related resources locally and on the Internet, with an emphasis on the computational aspects of the statistical models in order to harness the ever-growing hardware power. Upon finishing the course, the students will master advanced applications of statistical computing in a wide range of biological and biomedical problems. 3 credits Prerequisite: BMI 501 ; Basic knowledge in probability theory, algorithms and programming experience in R/MATLAB/C/C are expected. Knowledge in biology is a plus but not a must. Grading Letter graded (A, A-, B+, etc.)

Code

BMI 540

Credits

3

Subject

BMI

Programs That Include This Course

• Master's Master of Science in Biomedical Informatics