Instructor:  Arvind Ayyer 
Office:  X15 (new wing) 
Office hours:  TBD 
Phone number:  (2293) 3215 
Email:  (First name) at iisc dot ac dot in 
Class Timings:  Mondays, Wednesdays and Fridays, 10:00 — 11:00 
Classroom:  LH4 (new wing, first floor) 
Textbook: 
Introduction to Probability Models (11th edition)
by Sheldon M. Ross Academic Press, 2014 ISBN13  9789351072249 Supplementary Texts: (a) Probability and random processes by Geoffrey R. Grimmett and David R. Stirzaker Oxford University Press, 2001 ISBN13  9780198572220 (b) Markov Chains and Mixing Times by David A. Levin, Yuval Peres and Elizabeth L. Wilmer Markov Chains and Mixing Times ISBN13  9780812847398 
TA: 

Tutorials:  Thursdays 9:30 — 10:00 
The date for the midterms and final will be announced later.
Here are the weights for the homework and exams.
All marks will be posted online
on Moodle.
week  date  sections  material covered  homework and other notes 
1  2/8  1.11.2  Basic set theory  Chap. 1: 1, 3, 4, 5, 6 
2  5/8  1.31.4  Probabilities  Chap. 1: 8, 11, 12, 13, 15, 19, 21 
7/8  1.51.6  Independence  Chap. 1: 36, 37, 40, 43, 45, 47  
8/8    Quiz 1 
  
9/8    Holiday 

3  12/8    Holiday 

14/8  2.12.2  Discrete random variables  Chap. 2: 1, 2, 4, 5, 9, 16, 17, 20, 30  
15/8    Holiday 
  
16/8  2.3  Continuous random variables  Chap. 2: 33, 34, 35, 36, 38  
4  19/8  2.4  Expectation  Chap. 2: 39, 40, 41, 47 
21/8  2.5  Functions of random variables  Chap. 2: 46, 47, 48  
22/8    Quiz 2 
  
23/8  2.5  Joint random variables  Chap. 2: 49, 50, 53, 55  
5  26/8  2.5  Independence and covariance  
28/8  2.6  Moment generating functions  
29/8    Quiz 3 
  
30/8  2.6  Moment generating functions  
6  2/9    Holiday 

4/9  2.8  Limit theorems  
5/9    Quiz 4 
  
6/9  2.9  Stochastic processes  
7  9/9  3.13.3  Conditional probability  
11/9  3.4  Expectations by conditioning  
12/9    Quiz 5 
  
13/9  3.4  Conditional Variance formula  
8  16/9  3.5  Probabilities by conditioning  
18/9  3.6  A random graph model  
19/9    Quiz 6 
  
20/9  4.14.2  Introduction to Markov chains  
9  23/9  No class (midterm week) 

25/9  Midsemester exam  
26/9    No class (midterm week) 
  
27/9  No class (midterm week) 

10  30/9  4.2  Restrictions of Markov chains  
2/10    Holiday 

3/10    Quiz 7 
  
4/10  4.3  Classification of states  
11  7/10  4.4  Long run proportions  
9/10  4.4  Stationary distribution  
10/10    Quiz 8 
  
11/10  4.6  Examples of Markov chains  
12  14/10  
16/10  
17/10    Quiz 9 
  
18/10  
13  21/10  
23/10  
24/10    Quiz 10 
  
25/10  
14  28/10  
30/10  
31/10    Quiz 11 
  
1/11    Holiday 

15  4/11  
6/11  
7/11    Quiz 12 
  
8/11  
16  11/11  
13/11  
14/11    Quiz 13 
  
15/11  
16  18/11  
20/11  
21/11    Quiz 14 
  
22/11 