A firm mathematical foundation for work in biostatistics is provided by a detailed consideration of four mathematical frameworks that can be applied throughout medicine. The class will focus on these framework ideas, which build on earlier coursework in statistics and probability, and their applications. The mathematical frameworks are Markov models, Gaussian processes, logistic regression, and Bayesian networks. The clinical settings to be explored will be associated with treatment, prognosis, and survival within the settings of asthma, diabetes, cancer, and epidemics. While the course is primarily mathematical, students will be expected to work within at least one programming environment (R or Python will be easiest, but Julia, MATLAB, and others will also be supported).
Course Prerequisite(s)
EN.625.603 Statistical Methods and Data Analysis or equivalent. Ability to work within R, Python, Julia, or MATLAB or similar code settings for analysis of data and code development.
Course Offerings
There are no sections currently offered, however you can view a sample syllabus from a prior section of this course.