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.
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 Calculus. Those 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).
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.
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 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).
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.
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.
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 |