Thread by Levi: The math behind Bayes' Theorem clearly explained! ðŸ§µ twitter.com/levikul09/status/1634860542333685761/photo/1
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 Mar 12, 2023
 #Math #Probability
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In this ðŸ§µ We will calculate with the numbers from the disease example.
You can find the example here:
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You can find the example here:
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We want to know:
What is the probability that you have the disease given that you have tested positive?
Mathematically,
P(A  B) = P(You have disease  Tested positive)
So
A = sick
B = tested positive
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What is the probability that you have the disease given that you have tested positive?
Mathematically,
P(A  B) = P(You have disease  Tested positive)
So
A = sick
B = tested positive
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Now let's do the math.
1. Calculate P(A) namely the probability of being sick, this is our first prior.
Note: we can calculate this from the table, or from the given information:
We know that 1 out of every 10,000 patients is sick.
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1. Calculate P(A) namely the probability of being sick, this is our first prior.
Note: we can calculate this from the table, or from the given information:
We know that 1 out of every 10,000 patients is sick.
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Now we can put everything together ðŸ”½
The probability of being sick when you are tested sick is less than 1%.
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The probability of being sick when you are tested sick is less than 1%.
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That's it for today.
I hope you've found this thread helpful.
Like/Retweet the first tweet below for support and follow @levikul09 for more Data Science threads.
Thanks
8/8
I hope you've found this thread helpful.
Like/Retweet the first tweet below for support and follow @levikul09 for more Data Science threads.
Thanks
8/8
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Great one, Levi