1.203
Applied Probability and Stochastic Models
A vigorous use of probabilistic models to approximate real-life situations in Finance, Operations Management, Economics, and Operations Research. Emphasis on how to develop a suitable probabilistic model in a given setting and, merging probability with statistics, and on how to validate a proposed model against empirical evidence. Extensive treatment of Monte Carlo simulation for modeling random processes when analytic solutions are unattainable.
Fall 2006 · Logistical and Transportation Planning Methods
Topics
- Introduction, Overview
- Pedestrian Crossing Problem 1
- Pedestrian Crossing Problem 2
- Functions of Random Variables 1
- Functions of Random Variables 2
- Geometrical Probability 1
- Geometrical Probability 2
- Introduction to Queues; Little’s Law
- Markov Birth-and-Death Queues
- Some Transportation Problems
- More General Markov Queues
- The M/G/1 Queue and Extensions
- Congestion Pricing
- Hypercube Queueing Model 1
- Queueing Networks; Dynamic Queues
- Hypercube Queueing Model 2
- Hypercube in Practice
- Introduction to Networks
- Node and Edge Covering
- Location Problems
- Probabilistic Routing and Location
- Simulating Randomness
- Networks Research from Urban OR
- Implementation
- Final Roundup
Readings
- The textbook will be:
- Larson, Richard C., and Amedeo R. Odoni. Urban Operations Research . Belmont, MA: Dynamic Ideas, 2007. ISBN: 0975914634.
Prerequisites & course information
Same subject as 15.073[J] , IDS.700[J] Prereq: 6.3700 or 18.600 G (Fall) Not offered regularly; consult department 3-0-9 units
Sources
- Course catalogue ↗2026–2027
- MIT OpenCourseWare ↗Fall 2006
- MIT OCW calendar ↗
- MIT OCW syllabus ↗
- Historical MIT OpenCourseWare offering. Named reading entries and schedule topics are automatically extracted from the linked source pages.