Spring 2017 - Ani Adhikari

Developed by Ani Adhikari, Dibya Ghosh, Jim Pitman, Maxwell Weinstein, & Jason Zhang

Announcements! ( See All )
Apr 28: Final Exam Resources page is up
Apr 28: The Final will be held on Friday 5/12 from 7-10 PM
Apr 25: No lab this week
Mar 6: Midterm has been posted on Gradescope. See bCourses for a summary of midterm performance.
Feb 1: Please don't delete cells in homework notebook! Save before exporting pdf!
Jan 17: Join Piazza for this class! (Search for Prob 140)
Jan 17: If the math in the Course Notes doesn't render properly, refresh/reload the page.

Week 0 Overview

Getting Started

Before January 17

Week 1

Fundamentals

1/16-1/20

Week 2

Random Counts

1/23-1/27

Week 3

Conditioning and Bayes Methods

1/30-2/3

Week 4

Introduction to Prediction

2/6-2/10

Week 5

Markov Chains

2/13-2/17

Week 6

Conditioning and Bayes Revisited

2/20-2/24

Week 7

Midterm

2/27-3/3

Week 8

Variability of Sample Sums

3/6-3/10

Week 9

A Continuum of Values

3/13-3/17

Week 10

Families of Densities

3/20-3/24

Week 11

An Approach to Inference

4/3-4/7

Week 12

A Different Approach to Inference

4/10-4/14

Week 13

Prediction

4/17-4/21

Week 14

A Multivariate Model

4/24-4/28

WeekDateTopicCourse NotesPractice Assignment
1 Tue 1/17 Probability: axioms, rules, approximation Ch 1: Fundamentals
Ch 2: Calculating Chances
Guide HW 1
Th 1/19 Random variables: distributions, conditioning, equality Ch 3: Random Variables
Ch 4: Relations Between Variables
Lab 1: Matches in Random Sampling
2 Tue 1/24 Induction, counting, and bounds Ch 5: Collections of Events
Guide HW 2
Th 1/26 Binomial and Multinomial Distributions; The Poisson Limit Ch 6: Random Counts
Lab 2: Poisson Approximations in Random Permutations
3 Tue 1/31 Independence and Poissonization; dependence and symmetry Ch 7: Poissonization
Ch 8: Dependence
Guide HW 3
Wed 2/1 Quiz 1
Th 2/2 Prior and posterior distributions; random sampling Lab 3: Riffle Shuffles and Randomness
4 Tue 2/7 Expectation: properties and tail bounds Ch 9: Expectation
Guide HW 4
Th 2/9 SD: properties, tail bounds, least squares Ch 10: Standard Deviation
Lab 4: Minimum, Median, and Maximum
5 Tue 2/14 Probabilities and expectation by conditioning Ch 11: Conditioning Revisited
Guide HW 5
Wed 2/15 Quiz 2
Th 2/16 Long run behavior of Markov Chains Ch 12: Markov Chains
Lab 5: Tsetlin Library and PageRank
6 Tue 2/21 Hitting and occupation times Ch 13: Hitting and Occupation Times
Midterm Guide HW 6
Th 2/23 Reversibility and MCMC Ch 14: Reversing Markov Chains
Problems Lab 6: Codebreaking by MCMC
7 Tue 2/28 Review No HW 7
Th 3/2 Midterm No Lab 7
8 Tue 3/7 Covariance and its uses Ch 15: Variance Via Covariance
Guide HW 8
Th 3/9 Central Limit Theorem Ch 16: The Central Limit Theorem
Lab 8: Binomial, Poisson, and Normal
9 Tue 3/14 Probability densities Ch 17: Continuous Distributions
Guide HW 9
Th 3/16 Transformations Ch 18: Transformations
Lab 9: Simulation and the CDF
10 Tue 3/21 Joint densities Ch 19: Joint Density
Guide HW 10
Wed 3/22 Quiz 3
Th 3/23 The Beta Family No Lab 10 (Spring Break)
11 Tue 4/4 Independent normals; maximum likelihood Ch 20: Applications of the Normal
Guide HW 11
Th 4/6 Sums and the gamma family Lab 11: MLE, Chi-Squared, and Goodness of Fit
12 Tue 4/11 Moment generating functions; Chernoff bound Ch 21: Distributions of Sums
Guide HW 12
Wed 4/12 Quiz 4
Th 4/13 Prior and posterior densities; beta-binomial Ch 22: Prior and Posterior Distributions
Lab 12: Chinese Restaurant Process I
13 Tue 4/17 Best predictor Guide HW 13
Th 4/21 Best linear predictor: correlation and regression Ch 23: Prediction
Lab 13: Chinese Restaurant Process II
14 Tue 4/25 The multivariate normal Ch 24: The Bivariate Normal Distribution
Final Exam Resources No HW due in RRR week
Th 4/27 Conclusion