View: https://old.reddit.com/r/IncelTears/comments/1gp29mp/pim_pmi_pi_pm/
The probability this guy is an incel in denial = 100%
View: https://old.reddit.com/r/IncelTears/comments/1gp29mp/pim_pmi_pi_pm/
The probability this guy is an incel in denial = 100%
What is this retard talking about
100% coping mathcel or betabuxxed mathcel
One of us,one of us,one of us,
Lmao these faggots are engineering a misogyny detector. Fucking complete losers to have the time to come up with something like that, moggers don’t have the time nor do they care. Literal subhumans in denial, the leftwing of incels I should say, mixed in with the occasional foid or troon
why is he wasting time doing this he should be fucking his gf or spending time with her since he is not inkel
I can't find the purpose neither make sense of whatever he is spewing. Truly over for Autists.
Same question
No theorem for your face
Alright, let’s use the original setup and apply hypothetical values to each probability to illustrate how Bayes' theorem works with the given context.
Here's what we’re working with:
1. \( P(\text{I|M}) \): Probability a person is an incel given that they are a misogynist.
2. \( P(\text{M}) \): Base rate of misogyny (probability a random person is a misogynist).
3. \( P(\text{M|I}) \): Probability a person is a misogynist given that they are an incel.
4. \( P(\text{I}) \): Base rate of incels (probability a random person is an incel).
### Hypothetical Values
Let's assign some hypothetical values to these probabilities:
- \( P(\text{I|M}) = 0.5 \): There's a 50% chance that a misogynist is also an incel.
- \( P(\text{M}) = 0.3 \): 30% of the population are misogynists.
- \( P(\text{I}) = 0.1 \): 10% of the population are incels.
We want to find \( P(\text{M|I}) \): the probability that someone is a misogynist given that they are an incel.
### Applying Bayes' Theorem
Using Bayes' theorem, we have:
\[
P(\text{M|I}) = \frac{P(\text{I|M}) \cdot P(\text{M})}{P(\text{I})}
\]
Plugging in our hypothetical values:
\[
P(\text{M|I}) = \frac{0.5 \cdot 0.3}{0.1}
\]
1. First, multiply \( P(\text{I|M}) \) and \( P(\text{M}) \):
\[
0.5 \cdot 0.3 = 0.15
\]
2. Then, divide by \( P(\text{I}) \):
\[
\frac{0.15}{0.1} = 1.5
\]
So, \( P(\text{M|I}) = 1.5 \), or 150%. This result suggests that, given someone is an incel, it’s very likely (even more than 100%, indicating an assumed overlap or correlation beyond the base rates) they are also a misogynist under these hypothetical values.
Alright, let’s use the original setup and apply hypothetical values to each probability to illustrate how Bayes' theorem works with the given context.
Here's what we’re working with:
1. \( P(\text{I|M}) \): Probability a person is an incel given that they are a misogynist.
2. \( P(\text{M}) \): Base rate of misogyny (probability a random person is a misogynist).
3. \( P(\text{M|I}) \): Probability a person is a misogynist given that they are an incel.
4. \( P(\text{I}) \): Base rate of incels (probability a random person is an incel).
### Hypothetical Values
Let's assign some hypothetical values to these probabilities:
- \( P(\text{I|M}) = 0.5 \): There's a 50% chance that a misogynist is also an incel.
- \( P(\text{M}) = 0.3 \): 30% of the population are misogynists.
- \( P(\text{I}) = 0.1 \): 10% of the population are incels.
We want to find \( P(\text{M|I}) \): the probability that someone is a misogynist given that they are an incel.
### Applying Bayes' Theorem
Using Bayes' theorem, we have:
\[
P(\text{M|I}) = \frac{P(\text{I|M}) \cdot P(\text{M})}{P(\text{I})}
\]
Plugging in our hypothetical values:
\[
P(\text{M|I}) = \frac{0.5 \cdot 0.3}{0.1}
\]
1. First, multiply \( P(\text{I|M}) \) and \( P(\text{M}) \):
\[
0.5 \cdot 0.3 = 0.15
\]
2. Then, divide by \( P(\text{I}) \):
\[
\frac{0.15}{0.1} = 1.5
\]
So, \( P(\text{M|I}) = 1.5 \), or 150%. This result suggests that, given someone is an incel, it’s very likely (even more than 100%, indicating an assumed overlap or correlation beyond the base rates) they are also a misogynist under these hypothetical values.
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