Math 668 Stochastic Processes and Probability
Winter 2015

Instructor:
I. H. Dinwoodie, M327 Neuberger Hall (NH)
Office Hours:
Th: 11:00 12:00 AM
Fr: 1:30-2:30 PM
Lecture:
MWF 11:30-12:20 ASRC 215
Overview:
This course is on mathematical probability with foundations in analysis and measure theory. The presentation will include applications of stopping times, extreme value distributions, and Ito equations. We will also do some computing exercises to illustrate approximations and limit theorems.
Required Texts:
  1. R. Durrett, Probability: Theory and Examples 4th Edition, Cambridge University Press, 2013. Math 668 will cover approximately Chapters 3.1-3.3, 5.1-5.3 (martingales), 6.1-6.8 (Markov chains), with occasional forays into Oksendal for variety. Oksendal will be used more in 669.
  2. B. Oksendal, Stochastic Differential Equations: An Introduction with Applications, Springer, 2010. (Earlier editions should work too.)
Prerequisites:
Math 667, Advanced calculus and a course in probability or mathematical statistics.
Midterm Exam: due Feb 6
Final Exam: Thursday March 19 12:30-2:20 (here)
Homework:
  1. Friday Jan 9: 3.3.9 (note: x^2 is not bounded!), 3.3.10
  2. Friday Jan 16: 5.1.2, 5.1.3, 5.1.9
  3. Friday Jan 23: 5.2.6, 5.2.9 and apply it to Y_n = e^(t Z_n)/mgf(t), Z_n Bernoulli p (this is closely related to Wald's identity for stopping a sum at a random time, which will follow from the optional stopping theorem) (some solutions)
  4. Friday Jan 30: 5.2.4, 5.3.13
  5. Friday Feb 6 Midterm
  6. Friday Feb 13: plot the 8 trajectories of the chromatic number martingale with respect to the vertex exposure filtration, using p=1/2 for including edges and 3 vertices, and verify that increments are bounded by 1, sketch of solution
  7. Friday Feb 20: 6.2.4, 6.2.9 note: 6.2.9 is the DAG model for exchangeability of X_i. Use the Beta density to get the integrals to compute the transition function. The transition probabilities are given by P(S_{n+1}-S_{n}=1 | X_1, ..., X_n) = ( [S_{n}+n]/2 + 1 )/ (n+2), or in our books notation p_n(i,i+1)=([i+n]/2 + 1)/(n+2), p_n(i,i-1) = ([n-i]/2 + 1)/(n+2) careful with notation.
  8. Friday Feb 27: 6.3.10 (apply to A={a,b} for symmetric random walk to get expected time-to-ruin), 6.3.11
  9. Friday Mar 6: 6.5.10 and apply to symmetric rw on Z, 6.6.1.




                       
    January 2015    
Su Mo Tu We Th Fr Sa
             1  2  3 
 4  5  6  7  8  9 10	3.3
11 12 13 14 15 16 17 	5.1
18 19 20 21 22 23 24 	5.2, Monday holiday 
25 26 27 28 29 30 31 	5.2
                     
    February 2015   
Su Mo Tu We Th Fr Sa
 1  2  3  4  5  6  7 	5.3
 8  9 10 11 12 13 14 	5.7 Optional Stopping Theorem 
15 16 17 18 19 20 21 	6.1-6.2, Bad Gibbs Sampler 
22 23 24 25 26 27 28	6.3-6.4
                     
                     
     March 2015     
Su Mo Tu We Th Fr Sa
 1  2  3  4  5  6  7 	6.5
 8  9 10 11 12 13 14 	6.6, 6.8
15 16 17 18 19 20 21 	final exams 
22 23 24 25 26 27 28 
29 30 31             
                     

last updated: Mar 5 2015