Bifalgorithm post · Brisk · Chapter 1

How does Bayes’ Theorem make difficult probabilities easier to estimate?

How does Bayes’ Theorem make difficult probabilities easier to estimate? (Brisk · Chapter 1) — "Another trick that helps compute probabilities in practice is B…

Ideas in this flowchart

Le théorème de Bayes permet de calculer des probabilités en pratique.
La formule se construit à partir des probabilités conditionnelles.
Pr(A | K) = Pr(A ∩ K) / Pr(K)
En échangeant A et K, on obtient Pr(K | A) = Pr(K ∩ A) / Pr(A).
La combinaison de ces relations par l’algèbre donne Pr(A | K) = Pr(K | A) Pr(A) / Pr(K).
Cette équation a de nombreuses applications en informatique et en statistique.
Elle exprime Pr(A | K), souvent difficile à estimer, à partir de Pr(K | A), souvent plus facile à estimer.

Bifalgorithm

Feed/

How does Bayes’ Theorem make difficult probabilities easier to estimate?

Channel

Brisk

1 page

USR292600b83e2a

Ebook3w

"Another trick that helps compute probabilities in practice is Bayes’ Theorem. We’ve defined Pr( A | K ) as Pr ( A ∩ K ) Pr ( K ) , and by swapping the letters we get Pr( K | A ) = Pr ( K ∩ A ) Pr ( A ) . Combining these with a little bit of algebra yields: Pr ( A | K ) = Pr ( K | A ) Pr ( A ) Pr ( K ) Now this is a very, very powerful equation that has a multitude of uses throughout computer science and statistics. What makes it powerful is that it allows us to express Pr( A | K ), a quantity often very difficult to estimate, in terms of Pr( K | A ), which is often much easier."

Flowchart preview

👍

0 comments0 shares