STAT 150: Stochastic Processes (Spring 2015)

Practice Final: pdf Solutions

Midterm 1: Solutions Midterm 2: Solutions Practice Midterm 1: Solutions Midterm 2: Solutions

RRR Week Schedule:
Monday 12-1, 150 GSPP Review Session
Monday Section as usual
Tuesday 12-1 Leconte 0003 Problem Session with Jonathan
Wednesday 12-1, 150 GSPP Jonathan will go over the practice final solutions
New:Thursday 11-12:30 Evans 344 I will be giving an extra problem session
Office hours as usual

New: Piazza: [Signup]

Instructor: Allan Sly

Course Syllabus: Syllabus

Class Time: MWF 12:00 - 1:00 PM in room 150 Goldman School of Public Policy .

Section: Monday 3:00 - 4:00 PM or 4:00 - 5:00PM 332 Evans Hall.

GSI: Jonathan Hermon.

Office Hours Instructor: Tuesday 3:30-4:30 and Tuesday 10:30-11:30 Evans Hall 333.
GSI: Wednesday 5-7 and Friday 3-5 Evans 444.

Email Instructor: sly@stat (with the obvious ending)
GSI: (jonathan.hermon@stat)

Midterms Wednesday February 25 and April 8 in class.

Exam May 13, 3:00-6:00PM

Schedule

Lecture 1, January 21: Course introduction and Review of Stat 134 material.

Lecture 2, January 23: Review of Stat 134 material continued - random variables and conditional probabilities and expectations.

Lecture 3, January 26: Wald's Identity. See Pitman's [notes] .

Lecture 4, January 28: Martingales. See Pitman's [notes] .

Lecture 5, January 30: Markov Chains. See Pitman's [notes] or Section 4.1 of Gallager.

Lecture 6, February 2: Markov Chains continued.

Lecture 7, February 4: Long run behaviour of Markov chains. Notes by Pitman [notes] , [notes2] or Section 4.1 of Gallager 4.2 and 4.3.

Lecture 8, February 6: Long run behaviour of Markov chains continued.

Lecture 9, February 9: Long run behaviour of Markov chains continued.

Lecture 10, February 11: Long run behaviour of Markov chains continued.

Lecture 11, February 13: Long run behaviour of Markov chains continued, birth and death chains.

Lecture 12, February 18: First step analysis. Notes by Pitman [notes] , [notes2]

Lecture 13, February 20: First step analysis continued.

Lecture 14, February 23: Markov Chain Monte Carlo MCMC Notes .

Lecture 15, February 25: Midterm.

Lecture 16, February 27: Markov Chain Monte Carlo continued.

Lecture 17, March 2: Generating functions. Notes by Pitman [notes]

Lecture 18, March 4: Branching Processes. Notes by Pitman [notes]

Lecture 19, March 6: Branching Processes continued.

Lecture 20, March 9: Poisson Processes. Notes by Pitman [notes]

Lecture 21, March 11: Poisson Processes.

Lecture 22, March 13: Poisson Processes.

Lecture 23, March 16: Poisson Processes

Lecture 24, March 18: Continuous time Markov Chains. Notes by Pitman [notes] and [notes]

Lecture 25, March 20: Continuous time Markov Chains.

Lecture 26, March 30: Continuous time Markov Chains.

Lecture 27, April 1: Continuous time Markov Chains.

Lecture 28, April 3: Continuous time Markov Chains.

Lecture 29, April 6: Queueing Theory. Notes by Pitman [notes]

Lecture 30, April 8: Midterm

Lecture 31, April 10: Renewal Theory. Notes by Pitman [notes]

Lecture 32, April 13: Renewal Theory.

Lecture 33, April 15: Gaussian Processes.

Lecture 34, April 17: Brownian Motion. Notes by Pitman [notes] and [notes] .

Lecture 35, April 20: Brownian Motion.

Lecture 36, April 22: Brownian Motion.

Lecture 37, April 24: Brownian Motion.

Lecture 38, April 29: Brownian Bridge. Notes by Pitman [notes]

Homework

Homeworks will be posted on Fridays and due in the following Fridays. The first will be assigned in week 2 and due in week 3.

Week 3: Due February 6: PDF Solutions

Week 4: Due February 13: PDF Solutions

Week 5: Due February 20: PDF Solutions

Week 6: No homework but here is a practice midterm PDF Solutions

Week 7: Due March 6: PDF Solutions

Week 8: Due March 13: PDF Solutions

Week 9: Due March 20: PDF Solutions

Week 10: Due April 3: PDF Solutions

Week 12: Due April 17: PDF Solutions

Week 13: Due April 24: PDF Solutions

Week 14: Due May 1: PDF Solutions

Section

Here are handouts and solutions for the weekly sections.

Section 2: : Handout

Section 3: Handout Solutions