Computational methods used in analysis of experimental data. Introduction to probability and random variables. Monte Carlo methods for simulation of random processes. Statistical methods for parameter estimation and hypothesis tests. Confidence intervals. Multivariate data classification. Unfolding methods. Examples primarily from particle and medical physics. Prerequisite(s): third year standing in a physics program and an ability to program in Python, Java, C or C++, and permission of the Department. Also offered at the graduate level, with different requirements, as PHYS 5002, for which additional credit is precluded. Lectures three hours a week.
Computational methods used in analysis of experimental data. Introduction to probability and random variables. Monte Carlo methods for simulation of random processes. Statistical methods for parameter estimation and hypothesis tests. Confidence intervals. Multivariate data classification. Unfolding methods. Examples primarily from particle and medical physics. Prerequisite(s): third year standing in a physics program and an ability to program in Python, Java, C or C++, and permission of the Department. Also offered at the graduate level, with different requirements, as PHYS 5002, for which additional credit is precluded. Lectures three hours a week.
