Middlebury
header image

MATH0410

Stochastic Processes

Stochastic Processes
Stochastic processes are mathematical models for random phenomena evolving in time or space. This course will introduce important examples of such models, including random walk, branching processes, the Poisson process and Brownian motion. The theory of Markov chains in discrete and continuous time will be developed as a unifying theme. Depending on time available and interests of the class, applications will be selected from the following areas: queuing systems, mathematical finance (Black-Scholes options pricing), probabilistic algorithms, and Monte Carlo simulation. (MATH 0310) 3 hrs. lect./disc.
Subject:
Mathematics
Department:
Mathematics
Division:
Natural Sciences
Requirements Fulfilled:
DED
Equivalent Courses:

Sections

Spring 2015

MATH0410A-S15 Lecture (Peterson)

Spring 2013

MATH0410A-S13 Lecture (Peterson)

Spring 2011

MATH0410A-S11 Lecture (Peterson)

Spring 2009

MATH0410A-S09 Lecture (Peterson)

Spring 2007

MATH0410A-S07 Lecture (Peterson)

Spring 2005

MATH0410A-S05 Lecture (Peterson)