Lectures schedule (L01)

Tutorials schedule (T01)

Monday/Wednesday/Friday
15:0015:50 (SS 113)

None 
Midterm
and Quizzes:
There will be 1
Midterm (October 28 Monday, 15:00am, ) and 2
Quizzes (Mondays, September 30 and November 25, 15:00am, ).
Final
Exam: Wed, Dec 18, 15:3017:30, ENE 243
It will cover all the materials covered in this
course.
Grading scheme (Course Evaluation):
Exam, Midterm, Quizzes and
Homework Assignments

Value (% of your final mark)

Dates

Midterm

30%

Oct 28, 2019, Monday, 15:00, SS 113 
Quizzes (2)

20%=2x10%

Sept 30 &
Nov 25, 2019, Monday, 15:00, SS 113 
Final Exam  50%  Wed, Dec 18, 15:3017:30,
ENE 243 
Homework Assignments 
Not for Credit but for your
practice 
Month 
Day 
Monday 
Day 
Wednesday 
Day 
Friday 

Sep 
6&9 
Lec1 (Friday, Sept 6)Lec2 (Monday, Sept 9): 1) Short History of Stochastic Processes; 2) Intro to Probability Theory (Complimentary Notes: Basics in Probability) 
11 
Lec2: Stochastic
Processes (2.9) 
13 
Lec3: Markov
Chains (MC): Introduction (4.1) 

Sep 
16 
Lec4: MC: ChapmanKolmogorov Equations (4.2)  18 
Lec5: MC: Classification of States (4.3): Accessible States, Classes, Irreducible MC  20 
Lec6: MC: Classification of States (4.3): Recurrent and Transient States; Random Walk  
Sep 
23 
Lec7: MC: Limiting Probabilities (4.4)  25 
Lec8: MC: Limiting Probabilities. ExampleInsurance Claim (4.4) (cont'd Lec7)  27 
Lec9: MC: Some Applications (4.5)  
SeptOct 
30 
Quiz#1 (based
on Lectures 17) 
2 
Lec10: MC: Mean Time Spent in Transient States (4.6)  4 
Lec11: Exponential distribution (5.2.1)  
Oct 
7 
Lec12: Poisson Process
(PP): Counting
Process ( 5.3.1) and Definition
of PP (5.3.2) 
9 
Lec13: PP: Waiting Time Distribution (5.3.3)  11 
Lec14: PP: Further Properties (5.3.4)  
Oct 
14 
Thanksgiving Day
(No Lecture) 
16 
Lec15: PP: Conditional Distribution of Arrival Times (5.3.5)  18 
Lec16: PP: Sampling of PP (5.3.5)  
Oct 
21 
Lec17: PP: Generalizations (5.4.1)  23 
Lec18: PP: Compound PP (5.4.2)  25 
Lec19: PP: Mixed PP (5.4.3)  
OctNov 
28 
MIDTERM (based
on Lectures 518) 
30 
Lec20: Continuoustime Markov Chains (CTMC): Intro (6.16.2)  1 
Lec21: CTMC: Birth& Death Processes (6.3) and Transition Probability Function (6.4)  
Nov 
4 
Lec22: CTMC: ChapmanKolmogorov Equations (6.4)  6 
Lec23: : CTMC: Limiting Probabilities (6.5) 
8 
Lec24: CTMC: Computing the
Transition Probabilities (6.8) 

Nov 
11 
Fall Break  14 
Fall Break  16 
Fall Break  
Nov 
18 
Lec25: Renewal Theory: Intro (7.1), Renewal
Process, Properties. 
20 
Lec26: Renewal Theory: Some Distributions
(7.2), Renewal Equation. Limit Theorems (Optional)(7.3) 
22 
Lec27: Brownian Motion (BM) (10.1);Variations and Martingales
Properties (10.3) 

Nov 
25 
Quiz#2 (based
on Lectures 2027) 
27 
Lec28: BM: Pricing Stock Options and BlackScholes Formula (10.4.110.4.2)  29 
Lec29: BM: BlackScholes Formula's
and CallPut Parity's Derivations 

Dec 
2 
Lec30: BM: BlackScholes Formula's
Interpretation 
4 
Lec31: Stationary Processes (10.7)  6 
Lec32: Simulation Methods
(11.211.3) Table of Random Digits 
Final Exam Marks (out/100) * Please, use your @ucalgary.ca email to communicate with your instructor 
Homework Assignments (Solutions to Chapters_2_11) Laplace Table TableNormalDistribution TableExponentialFunction 