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Jul 13, 2018
Read 14 tweets

Bayes’ Theorem Definitions:

The vertical bar | stands for "given that".

P = Probability.

A & B are events.

P(A) & P(B) are the probabilities of events A and B. Each event is separate from the other.

P(A|B) is the probability of A being true given that event B is true.

#SoDS18 #ML

The vertical bar | stands for "given that".

P = Probability.

A & B are events.

P(A) & P(B) are the probabilities of events A and B. Each event is separate from the other.

P(A|B) is the probability of A being true given that event B is true.

#SoDS18 #ML

Example:

Say we have 2 coolers at an owambe: Cooler A is filled with 10packs of small chops only. Cooler B has 5packs of small chops and 5packs of Asun. You are then asked to close your eyes and pick a pack out of one cooler, which pack would you pick? #MachineLearning #SoDS18

Say we have 2 coolers at an owambe: Cooler A is filled with 10packs of small chops only. Cooler B has 5packs of small chops and 5packs of Asun. You are then asked to close your eyes and pick a pack out of one cooler, which pack would you pick? #MachineLearning #SoDS18

Because you know that we have more of small chops in both coolers, your brain is most likely going to tell you have picked a pack of small chops - even when your eyes are closed. This is not wrong.

#MachineLearning #SoDS18

#MachineLearning #SoDS18