MATH 402
Measure Theory and Probability Spring 2019 Division III; Quantative/Formal Reasoning;

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The study of measure theory arose from the study of stochastic (probabilistic) systems. Applications of measure theory lie in biology, chemistry, physics as well as in economics. In this course, we develop the abstract concepts of measure theory and ground them in probability spaces. Included will be Lebesgue and Borel measures, measurable functions (random variables). Lebesgue integration, distributions, independence, convergence and limit theorems. This material provides good preparation for graduate studies in mathematics, statistics and economics.
The Class: Type: lecture/discussion
Limit: 40
Expected: 30
Class#: 3723
Requirements/Evaluation: evaluation will be based primarily on performance on homework assignments and exams
Prerequisites: MATH 350 or MATH 351 or permission of instructor
Distributions: Division III; Quantative/Formal Reasoning;

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