Spring 2016: Introduction to Probability Modeling (36-410)

Instructor Information

Instructor: Siva Balakrishnan
Email: siva@stat.cmu.edu
Office Hours: Thursdays 1:30PM - 2:30PM
Location: BH 132K

TA Information

TA: Ilmun Kim
Email: ilmunk@andrew.cmu.edu
Office Hours: Mondays 3PM - 4PM
Location: BH 132M

TA: Zongge Liu
Email: zonggel@andrew.cmu.edu
Office Hours: Friday 2:30PM - 3:30PM
Location: BH 132M

Course Description

Stochastic processes are ways of quantifying the dynamic relationships of sequences of random events. Stochastic models play an important role in elucidating many areas of the natural, managerial, and engineering sciences. They can be used to analyze the variability inherent in biological and medical processes, to deal with uncertainties affecting managerial decisions, and with the complexities of psychological and social interactions, and to provide new perspectives, methodology, models and intuition to aid in other mathematical and statistical studies. This course in intended as an introduction to stochastic models for students familiar with elementary probability. The course aims to bridge the gap between a first course in mathematical probability and an intermediate level course in stochastic processes.

Course Syllabus

The syllabus provides information on grading, class policies etc.

Course Calendar:

The calendar has an approximate week-by-week schedule. Consult this document to know when the in-class exams are.

Lecture Notes

  • Lecture 1: (1/12) Introduction to the course
  • Lecture 2: (1/14) Probability review
  • Lecture 3: (1/19) Probability review contd.
  • Lecture 4: (1/21) Markov Chains 1
  • Lecture 5: (1/26) Markov Chains 2
  • Lecture 6: (1/28) Markov Chains 3
  • Lecture 7: (2/2) Markov Chains 4
  • Lecture 8: (2/4) Markov Chains 5

  • Annotated Lecture Notes

  • Lecture 2: Probability review
  • Lecture 3: Probability review contd.
  • Lecture 4: Markov Chains 1
  • Lecture 5: Markov Chains 2
  • Lecture 6: Markov Chains 3
  • Lecture 7: Markov Chains 4
  • Lecture 8: Markov Chains 5

  • Assignments

  • Assignment 1: Due in class on 1/19.
  • Assignment 2: Due in class on 1/26.
  • Assignment 3: Due in class on 2/2.
  • Assignment 4: Due in class on 2/9.

  • Assignment solutions

  • Assignment 1 Solutions
  • Assignment 2 Solutions