Probabilistic Analysis

Course Description

Concepts and tools for the analysis of problems under uncertainty, focusing on model building and communication: the structuring, processing, and presentation of probabilistic information. Examples from legal, social, medical, and physical problems. Spreadsheets illustrate and solve problems as a complement to analytical closed-form solutions. Topics: axioms of probability, probability trees, random variables, distributions, conditioning, expectation, change of variables, and limit theorems.

Course Details

  • Grading Basis: Letter Grade or Credit/No Credit
  • Unit-Range Information: Graduate students often prefer to take the course for 3 units.
  • Intensive Studies: This course is offered as part of the Data Science Intensive and must be taken for 4 units. See the Intensive Studies page for more information on how to receive an official Document of Completion.

Prerequisites

Multivariable Calculus; CME 100 or MATH 51

Syllabus Link

None available.
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