The course aims at providing the students with the basic tools for modelling and analysing situations that involve uncertainty and will consider applications in various fields. The course will develop a rigorous analysis of finite probability models and provide an introduction to infinite models. The course will cover the following topics: axioms of probability, conditional probability and independence, random variables, random vectors, probability distributions, properties of expectation and limit theorems.

Learning Objectives

At the end of the term, students will be able to:

  • Develop and evaluate simple probabilistic models of engineering systems.
  • Have a working knowledge of basics of probability: common distributions and processes, laws of probability and how to apply them, independence and conditional probability, common operations on random variables
  • Understand Central Limit Theorem and other limit theorems, and how to apply
  • Understand the basics of Poisson processes and its implications in understanding standard queueing models

Measurable Outcomes

  • Describe and explain the fundamental concepts of probability
  • Apply the laws of probability to engineering system problems with uncertainty
  • Apply limit theorems to draw inferences from data

12 Credits

Prerequisites:  10.004 Advanced Math II

Prerequisites (for Exchange Students): Multivariable Calculus

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