Stochastic Processes
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.
STATS 219
Units: 4
See When It's Offered opens a new window opens in a new tab
Typically Offered
Not Available
Grading Basis
Letter or Credit/No Credit
Class Level
Graduate
Instructional Mode
In Person
Gen Ed Requirement(s)
WAYS - Formal Reasoning (FR)