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)
Programs
MATH136
is a
completion requirement
for:
- (from the following course set: )
- (from the following course set: )