Everything You Ever Wanted to Know About Bayes' Theorem But Were Afraid To Ask.

Probability has an improbable history. Thomas Bayes deserves credit for introducing conditional probability but The Frequentists didn’t make it easy.

Wizard of Odds explores the slippery side of probability and the powerful role it plays in modern life. This program features Robert C. Green, Leonard Mlodinow, Masoud Mohseni, and Alan Peters.

Original program date: Saturday, May 30, 2015

Instagram: @worldscifest

[ascharest]

This video presents a nice example of the problem of false positives, especially when the baseline probability of having a disease is low. However, the comparison between the frequentist approach and the Bayesian approach is misguided here. Indeed, taking into account the baseline probability of Dengue into account is not "taking a Bayesian look", it is simply using Baye's theorem. A frequentist would compute the probability of the subject having the Dengue in the exact same way! The difference between frequentist and Bayesian statistics is much deeper than illustrated in this video.

[klausgartenstiel]

i'm glad that circumstances lead me to this video.

[henryunger]

Two points: 1. Until the twentieth century one could not have an appointment to a British school of higher learning unless you were ordained. No one says when mentioning Faraday or Maxwell, the most reverend. 2. Bayes was a member of the Royal Academy of Science, not so obscure to his peers.

[nimasanjabi]

It would help if you had included the definition of "False Positives, " or the definition of "Specificity".
The "Positive test results" are only 9.1% accurate, because the accurately diagnosed to have the disease results, "True Positives, " are mixed with the inaccurately diagnosed people of having the disease, "False Positives".
It might also be confusing for the viewer that why there is only one person with the disease, and why he is accurately diagnosed to have the disease?


Imagine we have 10, 000, 000 people, which only 0.01% of them have the disease.
And the test accuracy is 99.9%.


Total people ---> 10, 000, 000
Have the disease ---> 1000 ---> D
Don't have the disease ---> 9, 999, 000 ---> no-D


D people:
True positives: 999 of D will accurately be diagnosed positive.
False negatives: only 1 person of that 1000 D will inaccurately be diagnosed as negative.


no-D people:
True negatives: from the 9, 999, 000 no-D, 99.9% or 9, 989, 001 will be accurately diagnosed negative.
False positives: 9, 999 of no-D people will inaccurately get a positive test result.


Total true results = True positives + True negatives = 999 + 9, 989, 001 = 9, 999, 000
Total positives results = True positives + False Positives = 999 + 9, 999 = 10, 998
(True results) / (Total results) = 9, 999, 000 / 10, 000, 000 99.9% Test accuracy
(True positives) / (Total positives) = 999 / 10, 998 09.1% Positive results accuracy

[LaplacianFourier]

Needs citation on how Alan Turing used Bayes Theorem to crack the Enigma. No evidence of such thing happening is present.

[FAFAWI]

why the dislikes !!! people are a meystry

[mojojoji]

"Looking for free shwag" lol you mean X)

[josy]

99.9% of 10, 000 is 9990 not 9989, don't understand why you guys said 9989.

[Cosmicextry]

approximately 1 in 10 people are frequentists

[ModestConfidence]

The probability for dislikes is roughly 1 in 10 for this video currently. ;^)

[mehalrana]

Much easier to understand than 3B1B video.