Bayesian Statistics and Everyday Life

Connor Chapin

Steve

Steve is very shy and withdrawn, invariably helpful but with very little interest in people or in the world of reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail.

Steve

Steve is very shy and withdrawn, invariably helpful but with very little interest in people or in the world of reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail.

Which is more likely?

  1. Steve is a librarian.
  2. Steve is a farmer.

Steve

Steve is very shy and withdrawn, invariably helpful but with very little interest in people or in the world of reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail.

Which is more likely?

  1. Steve is a librarian.
  2. Steve is a farmer.

How can we think about this mathematically?

Bayes' Theorem!

Steve

  • Let's think about the main question:
    • Farmer or librarian?
  • There are 20-60x more farmers in the U.S. than librarians.
    • Say 20x.
  • With no additional evidence, 1/21 chance of being a librarian.
    • This is called our prior hypothesis.

Steve

  • We got new evidence in the statement:
    • "meek and tidy," "shy and withdrawn"
  • ​Let's suppose 50% of librarians have those traits and 10% of farmers.
  • How does this evidence update our hypothesis?
  • Bayes box

Bayes' Theorem

P(H|E) = \frac{P(E|H)P(H)}{P(E)}\\

Linda

Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.

Linda

Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.

Which is more likely?

  1. Linda is a bank teller.
  2. Linda is a bank teller and is active in the feminist movement.

Linda

Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.

Which is more likely?

  1. Linda is a bank teller.
  2. Linda is a bank teller and is active in the feminist movement.

Linda, reworded

Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.

There are 100 people who fit this description. How many are

  1. Bank tellers.
  2. Bank tellers that are active in the feminist movement.

Steve, reworded

  • The original wording wasn't exactly fair.
  • How was Steve chosen?
    • Do I personally know him?
  • Did I know his job when I wrote the description?
    • How did that influence the description?
  • Why were farmers and librarians chosen for the jobs?
    • Was it to imply one or the other? Which?

Steve, reworded

Steve is picked uniformly at random from the American population. I then interview Steve and write the previous description of him. Then, I select uniformly at random two jobs that exist in the United States.  

Which is more likely?

  1. Steve is a librarian.
  2. Steve is a farmer.

Envelope Auction

  • Earlier today, I rolled three dice.
  • After rolling the dice, I placed three different denominations of U.S. notes (paper money) using a secret method.
  • What do you want to bid for the envelope?

Envelope Auction

  • Was the answer what you expected?
    • Did you account for being wrong?
    • Did you account for me having more information?
  • Was the person who won "happy?"
    • Even without the sneaky answer, would they have been happy?
    • P(Guess<answer|highest guess)
  • This is a combination of adverse selection and the winner's curse.

Adverse Selection

  • Arises from people having imperfect information.
  • People only choose to do something when their information gives them an advantage.

More examples

  • Empty subway car/restaurant.
  • Buying or selling something (used car, stock).

Are Humans Bayesian?

Are Humans Bayesian?

  • Sometimes
  • Maybe we should be more Bayesian...

Are Humans Bayesian?

  • We are not perfect Bayesian networks
    • Cannot update beliefs every time
      • Exponential in number of variables
    • Can have "orphaned" beliefs

Are Humans Bayesian?

  • Aumann's Agreement Theorem

Are Humans Bayesian?

  • Confirmation bias in Bayesian terms
  • Ex:
    • Rowan says she saw a deer in Williamsburg
    • Dan says he saw a polar bear in Williamsburg
    • My prior: >99% chance deer exist in Williamsburg, <1% chance polar bears are here
    • Neither update my prior very much
      • But they do slightly
  • When could this fail?

COVID-19 Testing

  • 5-15% of U.S. infected with SARS-CoV-2
  • Some common tests:
    • 1%-15%-40% false negatives
    • 1%-10% false positives
  • H: Have COVID-19
  • E: Test positive
  • Bayes box
  • Chart of tests

Bayes

By Connor Chapin

Bayes

Presentation on basic auction theory

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