Description
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 taken primarily from particle and medical physics.
Prerequisites
- Prerequisite(s): an ability to program in Python, Java, C, or C+ +, and permission of the Department.
Conditions and arrangements
- Prerequisite(s): an ability to program in Python, Java, C, or C+ +, and permission of the Department.
- Also offered at the undergraduate level, with different requirements, as PHYS 4807 , for which additional credit is precluded.
Reference text in its original language
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 taken primarily from particle and medical physics.
- Prerequisite(s): an ability to program in Python, Java, C, or C+ +, and permission of the Department.
- Also offered at the undergraduate level, with different requirements, as PHYS 4807 , for which additional credit is precluded.
Sources and references
Dates and sources are retained to help you verify the information. Translations are provided to facilitate reading; the official source governs conditions and requirements.
Source reference : https://calendar.carleton.ca/grad/courses/PHYS/