|Homework (6 assignments)||40%|
Lectures: 2 sessions / week, 1.5 hours / session
Welcome to ESD.86, Models, Data, Inference for Socio-Technical Systems! This subject is part of the ESD PhD core, following the ESD doctoral seminar ESD.83 and requiring a solid undergraduate subject in applied probability as a prerequisite. At MIT the best prerequisite would be 6.041/6.431. With your help, this subject will be a great learning experience exposing you to interesting ideas, challenging you to think deeply, and providing skills useful in professional practice.
This is the first offering of this course, and is a work in progress. The instructors welcome suggestions for improvement. Please submit your suggestions via the OCW feedback form.
ESD.86 is a required subject for all ESD doctoral students. Its aim is to develop proficiency in developing and testing two types of models of systems operating under uncertainty:
We hope to show the strong linkages between applied probability and statistics in this subject. We believe that both types of modeling will be important in the research of most ESD doctoral students.
ESD.83 and 6.041
There are two 90-minute class sessions each week. We expect you to be present at these sessions and to participate thoughtfully. At the end of each week, our TA will offer an optional, guided office hour in which he will answer questions about that week's material and work through illustrative problems. Depending on how many students show up, this session may resemble a recitation, a tutorial or a one-on-one office hour's session.
Two books are recommended but not required for this class.
Larson, Richard C., and Amedeo R. Odoni. Urban Operations Research. 2nd ed. Belmont, MA: Dynamic Ideas, 2007. ISBN: 9780975914632. (A version of this text is also available online.)
Additional readings from other sources will be assigned during the term.
Each week there will be a contest relating to the worst use, misuse or abuse of statistics and probabilistic reasoning in the media - press, TV, blogs, radio, magazines, etc. Examples could be related to interpretations of statistical studies, or proposed new systems in which statistics plays a large role (here, likely unintended consequences of the 'reform' would be acceptable as part of the submission), etc.
Each Monday we will ask students to submit their nomination for the week. On Wednesday, after the teaching staff has had time to review the candidates, the 'winner' will be announced. This effort will be viewed as part of class participation. Plus winners will get the benefit of the doubt if their final grade is border-line. We intend for this to be both fun and educational.
There will be term projects. We have reserved the last two class periods for presentation of the projects. Details of the projects will be presented by end of February.
Computation is essential to modern analysis of systems operating under uncertainty. However, it is not a primary objective of this subject to develop your proficiency with any particular software tool. We have developed assignments, especially later in the subject, that require substantial computation, but we leave it to each student to select the software to use. Many of the computations that need to be carried out are statistical or probabilistic in nature. For example, one may need to generate samples drawn from a normally distributed population or may need to compute the standard deviation of a large data set or plot a histogram. A good choice for doing such tasks is MATLAB® and the associated Statistics Toolbox. Similar capabilities are embedded in Mathcad®, Maple®, and Mathematica®, as well as other software. One might be able to do all the assignments in a spreadsheet like Microsoft® Excel or QuattroPro®, but such tools may also limit the depth of the analysis that can be achieved. The assignments can also be done using programming language like C or FORTRAN, but this would probably be an inefficient use of time due to the needed coding. For those on campus, note that MATLAB® is available for use through the campus network. A further consideration in selecting computer tools for use in this subject is the level of support the teaching staff can provide. Prof. Frey can generally provide help with MATLAB®, MathCad®, MiniTab®, and Microsoft® Excel.
The manner in which you present your work can be just as important (and in some cases more so) than the overall approach manifested within the response. Be sure to clearly explain your work, the methods used, and the underlying assumptions. Such practices make it possible for us to fairly assess your work and happen also to be good practices for documenting work in industry.
It is expected that responses to assignments will be submitted on the due date and time noted on the assignment. The usual policy for late assignments is that a letter grade is lost per day late. In the case of unusual circumstances or unavoidable conflicts, please contact Prof. Larson or Prof. Frey to discuss the details and explore alternatives.
The units on an MIT subject correspond to the time that an adequately prepared student is expected to spend in a normal week. This is divided into three numbers associated with the subject (X-Y-Z) with X being class time, Y being laboratory time, and Z being work outside of class. The numbers associated with ESD.86 are (3-0-9) making this a 12-unit subject.
Your grade in ESD.86 will be determined based on your performance on homework, quizzes, a term project, and class participation as described in the table below:
|Homework (6 assignments)||40%|
This is a doctoral subject, and we expect everyone who works hard in the subject to receive either an A or a B.
|SES #||TOPICS||INSTRUCTORS||KEY DATES|
Introduction and overview
|Prof. Richard C. Larson and Prof. Daniel D. Frey|
Analyzing a probability problem
Probability mass functions
|Prof. Richard C. Larson|
Broken stick problem
Working in sample space
|Prof. Richard C. Larson|
|4||Pedestrian crossing problem||Prof. Richard C. Larson||Homework 1 due|
|5||Random incidence: A major source of selection bias||Prof. Richard C. Larson|
|6||Random incidence and more||Prof. Richard C. Larson|
|7||Spatial models||Prof. Richard C. Larson||Homework 2 due|
|8||Markov processes and their application to queueing, part 1||Prof. Richard C. Larson|
|9||Markov processes and their application to queueing, part 2||Prof. Richard C. Larson||Homework 3 due|
|10||Queueing and transitions: Sampling from distributions, Gauss||Prof. Richard C. Larson|
|11||Derived distributions to statistics||Aman Chawla (guest lecturer)||Homework 4 due|
The Queue Inference Engine and the psychology of queueing
Beyond the physics of queueing
|Prof. Richard C. Larson|
|13||The Weibull distribution and parameter estimation||Prof. Daniel D. Frey|
|14||Hypothesis testing||Prof. Daniel D. Frey|
|15||Descriptive statistics and statistical graphics||Prof. Daniel D. Frey|
|16||Regression||Prof. Daniel D. Frey||Homework 5 due|
|17||Analysis of variance, with discussion of Bayesian and frequentist statistics||Prof. Daniel D. Frey|
|18||Multiple regression||Prof. Daniel D. Frey|
Design of experiments, part 1
Design of experiments, part 2
|Prof. Daniel D. Frey||Homework 6 due|
|20||Design of computer experiments||Prof. Daniel D. Frey|
|21||Closure - Threats to validity of inference||Prof. Daniel D. Frey|
|22||Final presentations 1|
|23||Final presentations 2|
The fundamental principle of academic integrity is that one must fairly represent the source of the intellectual content of the work one submits for credit. Students are trusted to adhere to this principle and its meaning in the context of this subject as subsequently explained. Official Institute policy regarding academic honesty can be found in the current Bulletin under "Academic Procedures and Institute Regulations".
What is the policy on examinations? The examinations in this subject are to represent individual work. You may not receive any help from other students or any other individuals.
What about homework assignments? Can we work together? We encourage students to work together in this subject to understand the home assignments and to learn in general. There is much to be gained in sharing the learning process. However, the final submission should represent your own expression of the final response to the assignment and not a copy of someone else's expression thereof, whether directly from a person or as recorded on paper (e.g. a book) or electronically (e.g. on a Web site). Furthermore, you must fairly represent the authorship of the intellectual content of the work you submit for credit by acknowledging the contribution of sources (e.g., books, Web sites) you consult in the process of completing assignments. In addition, at the end of each assignment on which you collaborated with other students, you must cite the students and the interaction. The purpose of this is to acknowledge their contribution to your work. Some examples follow: