Stochastic Processes

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Course Description

Introduction to measure theory, Lp spaces and Hilbert spaces. Random variables, expectation, conditional expectation, conditional distribution. Uniform integrability, almost sure and Lp convergence. Stochastic processes: definition, stationarity, sample path continuity. Examples: random walk, Markov chains, Gaussian processes, Poisson processes, Martingales. Construction and basic properties of Brownian motion. Prerequisite: STATS 116 or MATH 151 or equivalent. Recommended: MATH 115 or equivalent. http://statweb.stanford.edu/~adembo/math-136/

Cross Listed Courses

Grading Basis

ROP - Letter or Credit/No Credit

Min

4

Max

4

Course Repeatable for Degree Credit?

No

Course Component

Lecture

Enrollment Optional?

No

This course has been approved for the following WAYS

Formal Reasoning (FR)

Courses

STATS219 is a antirequisite for:

Programs

STATS219 is a completion requirement for:
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