Shawn (Wanxiang) Zhong
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On this page

  • Textbook
  • Calendar
    • September
    • October
    • November
    • December
  • Lecture Outlines
    • Chapter 1: Experiment with Random Variables
    • Chapter 2: Conditional Probability and Independence
    • Chapter 3: Random Variables
    • Chapter 4: Approximations of the Binomial Distribution
    • Chapter 5: Transforms and Transformations
    • Chapter 6: Joint Distribution of Random Variables
    • Chapter 7: Sums and Symmetry
    • Chapter 8: Expectation and Variance in the Multivariate Setting
    • Section 6.4 & Section 8.5: Bivariate Normal Distribution
    • Chapter 10: Conditional Distribution
    • Chapter 9: Tail Bounds and Limit Theorems
    • Review
    • Lecture notes

Math 431 – Probability

Notes on Math 375: Introduction to the Theory of Probability @ University of Wisconsin-Madison
Published

October 31, 2017

Modified

February 13, 2018

Notes on Math 431: Introduction to the Theory of Probability Your comments and criticism are greatly welcomed.

Textbook

Introduction to Probability, by David F. Anderson,‎ Timo Seppäläinen,‎ Benedek Valkó

Calendar

September

Monday Wednesday Friday
 - 9/6 9/8
9/11 9/13 9/15
9/18 9/20 9/22
9/25 9/27 9/29

All notes from September in PDF format

October

Monday Wednesday Friday
10/2 10/4 10/6
10/9 10/11 10/13
Review Midterm 1 10/20
10/23 10/25 10/27
10/30  11/1 11/3

All notes from October in PDF format

November

Monday Wednesday Friday
11/1 11/3
11/6 11/8 11/10
11/13 11/15 11/17
11/20 11/22 Holiday
Review Midterm 2 12/1

All notes from November in PDF format

December

Monday Wednesday Friday
Review Midterm 2  12/1
12/4 12/6 12/8
12/11 12/13 Final

Lecture Outlines

Chapter 1: Experiment with Random Variables

Week 01 - 9/6: Introduction, 431 Card Game

Week 01 - 9/8: Probability Space, Equally Likely Outcome

Week 02 - 9/11: Equally Likely Outcome, Different Types of Random Experiments

Week 02 - 9/13: Equally Likely Outcome, Infinite Sample Spaces

Week 02 - 9/15: Three Rules of Sample Spaces

Week 03 - 9/18: Random Variables, Conditional Probability

Chapter 2: Conditional Probability and Independence

Week 03 - 9/20: Conditional Probability, Multiplication Rule, Partition

Week 03 - 9/22: Conditional Probability, Bayes’ Rule

Week 04 - 9/25: Independence, Bernoulli Distribution, Binomial Distribution

Week 04 - 9/27: Independence, Bernoulli Distribution, Binomial Distribution, Geometric Distribution, Conditional Independence

Chapter 3: Random Variables

Week 04 - 9/29: Birthday Paradox, Probability Mass Function, Probability Density Function

Week 05 - 10/2: Cumulative Distribution Function

Week 05 - 10/4: Conversion Between PMF/PDF and CDF

Week 05 - 10/6: Expectation

Week 06 - 10/9: Median, Variance, Standard Deviation, Affine Transformation of Random Variable

Week 06 - 10/11: Normal Distribution

Week 06 - 10/13: Normal Distribution

Week 07 - 10/16: Practice Midterm 1

Week 07 - 10/18: Midterm 1

Chapter 4: Approximations of the Binomial Distribution

Week 07 - 10/20: Expectation and Variance of Binomial Distribution, Normal Approximation of Binomial Distribution

Week 08 - 10/23: Normal Approximation of Binomial Distribution

Week 08 - 10/25: Central Limit Theorem, Law of Large Numbers, Confidence Intervals

Week 08 - 10/27: Poisson Distribution, Law of Rare Events, Exponential Distribution

Chapter 5: Transforms and Transformations

Week 09 - 10/30: Distribution of Function of Random Variables

Chapter 6: Joint Distribution of Random Variables

Week 09 - 11/1: Joint Distribution of Discrete Random Variables, Marginal Probability Mass Function, Multinomial Distribution

Week 09 - 11/3:  Joint Distribution of Continuous Random Variables, Uniform Distribution in Two Dimensions

Week 10 - 11/6: Uniform Distribution in Two Dimensions, Joint Distribution and Independence

Chapter 7: Sums and Symmetry

Week 10 - 11/8: Independent and Identically Distributed Random Variables, Sum of Independent Random Variables

Week 10 - 11/10: Negative Binomial Distribution, Convolution of Normal Random Variables, Linearity of Expectation

Chapter 8: Expectation and Variance in the Multivariate Setting

Week 11 - 11/13: Linearity of Expectation, Expectation and Independence, Expectation and Variance of Sample Mean, Mean of Sample Variance

Week 11 - 11/15: Covariance

Week 11 - 11/17: Independence and Covariance, Bilinearity of Covariance

Week 12 - 11/20: Correlation

Section 6.4 & Section 8.5: Bivariate Normal Distribution

Week 12 - 11/22: Standard Bivariate Normal Distribution, Bivariate Normal Distribution

Week 12 - 11/24: Thanksgiving Break

Week 13 - 11/27: Practice Midterm 2

Week 13 - 11/29: Midterm 2

Chapter 10: Conditional Distribution

Week 13 - 12/1: Conditional Distribution (Discrete Case)

Week 14 - 12/4: Conditional Distribution (Discrete Case)

Week 14 - 12/6: Conditional Distribution (Continuous Case)

Chapter 9: Tail Bounds and Limit Theorems

Week 14 - 12/8: Conditional Distribution (Continuous Case), Law of Large Numbers, Central Limit Theorem

Review

Week 15 - 12/11: Probability Space, Basic Rules of Probability, Conditional Probability, Independence, Random Variables

Lecture notes

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