Math/Stat 431 Introduction to the Theory of Probability - Lecture 1

Spring 2017, Lecture 1

Meetings: MWF, 9:55-10:45 AM Van Vleck B123
Instructor: Daniele Cappelletti
Office: 415 Van Vleck.
Office Hours: Thursday 9.00 - 10.30am, and by appointment.
E-mail:cappelletti@math.wisc.edu


This is the course homepage that also serves as the syllabus for the course. Here you will find our weekly schedule, and updates on scheduling matters. The Mathematics Department also has a general information page on this course.

I will use the class email list to send out corrections, announcements, etc.  Please check your wisc.edu email regularly.

Course description

Math 431 provides an introduction to the theory of probability, the part of mathematics that studies random phenomena. We model simple random experiments mathematically and learn techniques for studying these models. Topics covered include the axioms of probability, random variables, the most important discrete and continuous probability distributions, expectations, moment generating functions, conditional probability and conditional expectations, multivariate distributions, Markov's and Chebyshev's inequalities, laws of large numbers, and the central limit theorem.

431 is not a course in statistics. Statistics is a discipline mainly concerned with analyzing and representing data. Probability theory forms the mathematical foundation of statistics, but the two disciplines are separate.

From a broad intellectual perspective, probability is one of the core areas of mathematics with its own distinct style of reasoning. Among the other core areas are analysis, algebra, geometry/topology, logic and numerical analysis.

To go beyond 431 in probability you should take next 521 -- Analysis, and after that one or both of these: 632 Introduction to Stochastic Processes and 635 Introduction to Brownian Motion and Stochastic CalculusThose who would like a proof based introduction to probability could consider taking  Math 531 - Probability Theory (531 requires a proof based course as a prerequisite). 

Where is probability used?

Probability theory is used ubiquitously throughout the sciences and industry.  For example, in biology many models of cellular phenomena are now modeled probabilistically as opposed to deterministically (this is the subject area that got me interested in probability). As for industry, many models used by insurance companies are probabilistic in nature (see: actuarial science). Also, many of the models used by the finance industry are also probabilistic in nature (google "Black-Scholes" to see an example). Thus, those wishing to go into finance need to have a solid understanding of probability.

Prerequisites

To be technically prepared for Math 431 one needs to be comfortable with the language of sets and calculus, including multivariable calculus, and be ready for abstract reasoning. Basic techniques of counting is also useful, but we will review these along the way. Probability theory can seem very hard in the beginning, even after success in past math courses.   It is imperative that you be willing to put in the required effort.

Textbook

The course follows lecture notes by David Anderson, Timo Seppäläinen, and Benedek Valkó.  These will be provided to the students at no cost.  The textbook can be found at the Learn@UW website.  The following textbooks can be used as a resource for extra practice problems. They have been placed on reserve at the math library (floor B2 of Van Vleck).
Learn@UW

We will use the Learn@UW website of the course to post homework assignments and solutions. The lecture notes will also be posted there.

Piazza
We will be using Piazza for class discussion.  The system is catered to getting you help fast and efficiently from classmates and myself.  Rather than emailing math questions to me, I encourage you to post your questions on Piazza.  If you have any problems or feedback for the developers, email team@piazza.com.

Find our class Piazza page here.

Evaluation

Course grades will be based on homework and quizzes (15%),  two midterm exams (2x25%), and a comprehensive final exam (35%).  Your lowest quiz grade and your lowest homework grade will be dropped. As part of your homework, you are asked to use Webwork, an online platform where you can enter the solution of some exercises (Scroll down for more information). No calculators, cell phones, or other gadgets will be permitted in exams and quizzes, only pencils, pens and paper.
The final grades will be determined according to the following scale: 
A: [100,89), AB: [89,87), B: [87,76), BC: [76,74), C: [74,62), D: [62,50), F: [50,0]. 
There will be no curving in the class, but the instructor reserves the right to modify the final grade lines.

Bonus problems will be provided on the homework assignments.  Successful completion of these more challenging problems can lead to a few extra percentage points being added to your final grade.

Quizzes

To help prepare for the midterm exams we will have short in-class quizzes during the first few weeks. You will be able to find the quiz dates below.

The purpose of the quizzes is to give you practice answering probabilistic questions in a timed environment. We will have these quizzes until I decide they are no longer needed.  No makeup quizzes will be given, instead you may drop your lowest quiz score. 

Homework

Homework assignments will be posted on the Learn@UW site of the course. Weekly homework assignments are due Fridays at the beginning of the class. Note that there is a (short) homework assignment due on the first Friday (September 9).

Instructions for homework

Succeeding in this course: This course will be more difficult than previous math courses, although I strongly believe it will be more rewarding for those that put in the necessary effort. To succeed you should read the sections of the text before each lecture and then again after. You should work together in small groups and discuss the material.  (You may use Piazza to put a group together.) Such discussions are an invaluable way to learn the material. After working in the groups, you should carefully and clearly write-up your solutions making sure you actually understand the material. If, even after completing a homework assignment, you feel you still do not fully understand the material as well as you could (perhaps because you could not follow the discussion in your working group well), you should do more problems.

Webwork

During the course you will be asked to do some exercises on an online platform called Webwork. You can find it here: http://webwork.math.wisc.edu/webwork2
You will be provided with credential to access the relevant section. It may be a good idea to change your password once you have logged in.
To enter the solution, you just need to type it in. Webwork interface accepts numbers, fractions, functions, etc. , with the syntax that you would expect. While you do not have the possibility to explain your argument on Webwork, you can check whether your solution is correct, so you have an immediate feedback on whether you are on the right path. Also, if your Webwork solution is wrong, you can try again as many times as you like, until the due date. This can of course be helpful to you, as you may be able to assess what you understood on your own, and in case correct yourself before writing down the solution of the homeworks on paper.

Weekly schedule

Here is a tentative weekly schedule, to be adjusted as we go. The numbers refer to sections in lecture notes that can be found at the Learn@UW website.

Week
Monday
Wednesday
Friday
1 1/16-9/20

Appendix A Set notation. 
1.1 Sample spaces and probabilities.
HW 1 due.
1.2 Equally likely outcomes.
2 1/23-1/27
Quiz #1.
1.2 Equally likely outcomes.
1.3 Infinitely many outcomes.
HW 2 due.
1.4 Consequences of the rules of probability.
3 1/30-2/3
Quiz #2.
1.5 Random variables:a first look.
2.1 Conditional probability.
HW 3 due.
2.1 Conditional probability.
2.2 Bayes' formula.
4 2/6-2/10
Quiz #3.
2.3 Independence
2.3 Independence
HW 4 due.
2.4 Independent trials.
​​​​​
5 2/13-2/17
Quiz #4.
from 2.5: "Conditional independence"
3.1 Distributions of random variables
3.2 Expectations and variance.
HW 5 due.
3.2 Expectations and variance.
3.3 The Gaussian distribution.
6 2/20-2/24
3.3 The Gaussian distribution.
Exam 1 in evening.
3.3 The Gaussian distribution.
3.3 The Gaussian distribution.
7 2/27-3/3
3.3 The Gaussian distribution.
4.1 Normal approximation: The CLT for binomial and examples
4.1 Normal approximation: continuity correction, law of large numbers and confidence intervals.
HW 6 due.
4.1 Normal approximation: confidence intervals.
8 3/6-3/10
4.2 Poisson approximation
4.2 Poisson approximation
4.3 Exponential distribution
HW 7 due.
4.3 Exponential distribution
5.1 Moment generating functions
(skip 4.4 The Poisson Process)
9 3/13-3/17
5.1 Moment generating functions
5.2 Distribution of a function of a random variable
HW 8 due.
6.1 Joint distribution of discrete random variables.
6.3 Cumulative distribution function and expectations for several random variables (only Fact 6.41 and Example 6.42)
​​​​​-- Spring Break --
​​​​​
10 3/27-3/31
6.1 Joint distribution of discrete random variables.
6.2 Jointly continuous random variables
6.3 Cumulative distribution function and expectations for several random variables (only Fact 6.41)
HW 9 due.
6.2 Jointly continuous random variables
6.3 Cumulative distribution function and expectations for several random variables (only Example 6.44)
11 4/3-4/7
7.1 Sums of independent random variables.
7.1 Sums of independent random variables.
HW 10 due.
7.2 Exchangeable random variables.
(skipped 7.3 The Poisson Process)
8.1 Linearity of expectation.
12 4/10-4/14
8.2 Expectation and independence Coupon collector's problem (from section 8.2)
8.3 Convolution with moment generating functions.

Exam 2 in evening (on Thursday 13).
8.4 Covariance and correlation.
13 4/17-4/21
8.4 Covariance and correlation.
(skip 8.5 The bivariate normal distribution)
9.1 Estimating tail probabilities. HW 11 due.
9.2 Law of Large Numbers.
9.3 Central Limit Theorem.
14 4/24-4/28
10.1 Conditional distribution of discrete random variables. 10.1 Conditional distribution of discrete random variables.

HW 12 due.
10.2 Conditional distribution for jointly continuous random variables
15 5/1-5/5
10.2 Conditional distribution for jointly continuous random variables.
10.3 Further examples


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