Models, Data and Inference for Socio-Technical Systems

Photo of many cars at night stuck on a highway in Bangkok.

Sitting in traffic may be a fact of life in Bangkok and many other cities, but efficient queueing techniques discussed in this class can improve the efficiency of many lines in which we wait. (Photo courtesy of *keng.)

Instructor(s)

MIT Course Number

ESD.86

As Taught In

Spring 2007

Level

Graduate

Cite This Course

Course Features

Course Description

In this class, students use data and systems knowledge to build models of complex socio-technical systems for improved system design and decision-making. Students will enhance their model-building skills, through review and extension of functions of random variables, Poisson processes, and Markov processes; move from applied probability to statistics via Chi-squared t and f tests, derived as functions of random variables; and review classical statistics, hypothesis tests, regression, correlation and causation, simple data mining techniques, and Bayesian vs. classical statistics. A class project is required.

Frey, Daniel, and Richard Larson. ESD.86 Models, Data and Inference for Socio-Technical Systems, Spring 2007. (MIT OpenCourseWare: Massachusetts Institute of Technology), http://ocw.mit.edu/courses/engineering-systems-division/esd-86-models-data-and-inference-for-socio-technical-systems-spring-2007 (Accessed). License: Creative Commons BY-NC-SA


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