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Probability & statistics / English

Bayes' Theorem - The Simplest Case

Dr. Trefor Bazett · YouTube · 5:31

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The explanation, unpacked.

Reviewed learning material · Video analysis · English
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Bayes' theorem follows from writing the same intersection probability in two conditional orders. The lesson derives the formula and checks it with a two-child probability example.

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Chapters

0:50Start from conditional probability1:38Rearrange the shared intersection3:04Two children, at least one girl5:14Bayes' theorem gives one third

Learning script

Generated from the video's visuals and explanation; not verbatim speech.

For P(B)>0P(B)>0, conditional probability is P(A∣B)=P(A∩B)/P(B)P(A\mid B)=P(A\cap B)/P(B). Reversing the roles gives the corresponding expression for P(B∣A)P(B\mid A) when P(A)>0P(A)>0.

Since A∩B=B∩AA\cap B=B\cap A, we have P(A∩B)=P(B∣A)P(A)P(A\cap B)=P(B\mid A)P(A). Substitution gives P(A∣B)=P(B∣A)P(A)/P(B)P(A\mid B)=P(B\mid A)P(A)/P(B). The denominator normalizes the probability; it is not generally a prior-odds term.

The example assumes four equally likely ordered outcomes: GG,GB,BG,BBGG,GB,BG,BB. Conditioning on at least one girl removes BBBB, leaving three possibilities. This conclusion depends on that stated conditioning event, not on a different way of selecting or reporting a child.

Let AA be two girls and BB be at least one girl. Then P(B∣A)=1P(B\mid A)=1, P(A)=1/4P(A)=1/4 and P(B)=3/4P(B)=3/4, giving P(A∣B)=(1⋅1/4)/(3/4)=1/3P(A\mid B)=(1\cdot1/4)/(3/4)=1/3.

Knowledge cards

01

Start from conditional probability

For P(B)>0P(B)>0, conditional probability is P(A∣B)=P(A∩B)/P(B)P(A\mid B)=P(A\cap B)/P(B). Reversing the roles gives the corresponding expression for P(B∣A)P(B\mid A) when P(A)>0P(A)>0.

02

Rearrange the shared intersection

Since A∩B=B∩AA\cap B=B\cap A, we have P(A∩B)=P(B∣A)P(A)P(A\cap B)=P(B\mid A)P(A). Substitution gives P(A∣B)=P(B∣A)P(A)/P(B)P(A\mid B)=P(B\mid A)P(A)/P(B). The denominator normalizes the probability; it is not generally a prior-odds term.

03

Two children, at least one girl

The example assumes four equally likely ordered outcomes: GG,GB,BG,BBGG,GB,BG,BB. Conditioning on at least one girl removes BBBB, leaving three possibilities. This conclusion depends on that stated conditioning event, not on a different way of selecting or reporting a child.

04

Bayes' theorem gives one third

Let AA be two girls and BB be at least one girl. Then P(B∣A)=1P(B\mid A)=1, P(A)=1/4P(A)=1/4 and P(B)=3/4P(B)=3/4, giving P(A∣B)=(1⋅1/4)/(3/4)=1/3P(A\mid B)=(1\cdot1/4)/(3/4)=1/3.

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  • Bayes theorem ExplanationAt 0:50
    Why this connection?

    For P(B)>0P(B)>0, conditional probability is P(A∣B)=P(A∩B)/P(B)P(A\mid B)=P(A\cap B)/P(B). Reversing the roles gives the corresponding expression for P(B∣A)P(B\mid A) when P(A)>0P(A)>0. Since A∩B=B∩AA\cap B=B\cap A, we have P(A∩B)=P(B∣A)P(A)P(A\cap B)=P(B\mid A)P(A). Substitution gives P(A∣B)=P(B∣A)P(A)/P(B)P(A\mid B)=P(B\mid A)P(A)/P(B). The denominator normalizes the probability; it is not generally a prior-odds term. The example assumes four equally likely ordered outcomes: GG,GB,BG,BBGG,GB,BG,BB. Conditioning on at least one girl removes BBBB, leaving three possibilities. This conclusion depends on that stated conditioning event, not on a different way of selecting or reporting a child. Let AA be two girls and BB be at least one girl. Then P(B∣A)=1P(B\mid A)=1, P(A)=1/4P(A)=1/4 and P(B)=3/4P(B)=3/4, giving P(A∣B)=(1⋅1/4)/(3/4)=1/3P(A\mid B)=(1\cdot1/4)/(3/4)=1/3.

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