Why did it suddenly become subtractive? (Example of Bayes’ Theorem)

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In summary, the sudden shift to a subtractive approach in probability and decision-making can be illustrated through Bayes' Theorem. This theorem demonstrates how prior knowledge and new evidence combine to update the probability of an event. The subtractive aspect arises when considering the likelihood of competing hypotheses or outcomes, emphasizing the need to subtract the probabilities of alternative explanations to refine our understanding of a specific event. This process highlights the dynamic nature of probability assessment and the importance of integrating new information effectively.
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Rev. Cheeseman
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From https://corporatefinanceinstitute.com/resources/data-science/bayes-theorem/#:~:text=Formula for Bayes' Theorem&text=P(A|B) –,given event A has occurred

Example of Bayes’ Theorem
Imagine you are a financial analyst at an investment bank. According to your research of publicly-traded companies, 60% of the companies that increased their share price by more than 5% in the last three years replaced their CEOs during the period.

At the same time, only 35% of the companies that did not increase their share price by more than 5% in the same period replaced their CEOs. Knowing that the probability that the stock prices grow by more than 5% is 4%, find the probability that the shares of a company that fires its CEO will increase by more than 5%.

Before finding the probabilities, you must first define the notation of the probabilities.

P(A) – the probability that the stock price increases by 5%
P(B) – the probability that the CEO is replaced
P(A|B) – the probability of the stock price increases by 5% given that the CEO has been replaced
P(B|A) – the probability of the CEO replacement given the stock price has increased by 5%.
Using the Bayes’ theorem, we can find the required probability:

Sample Calculation

P(A l B) = 0.60 x 0.04/0.60 x 0.04 + 0.35 x (1 - 0.04) = 0.067 or 6.67%

Thus, the probability that the shares of a company that replaces its CEO will grow by more than 5% is 6.67%.

Sorry but notice the bold numbers, how did (1 - 0.04) appear there? I can't find 1 mentioned in the question? English is not my native language.
 
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Hi,

can you fix the errors?

0.60 x 0.04/0.60 x 0.04 + 0.35 + (1 - 0.04) =1.3116

so something isn't right.

1-0.04 is the probability that the stock prices do NOT grow by more than 5%

##\ ##
 
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  • #3
BvU said:
Hi,

can you fix the errors?

0.60 x 0.04/0.60 x 0.04 + 0.35 + (1 - 0.04) =1.3116

so something isn't right.

1-0.04 is the probability that the stock prices do NOT grow by more than 5%

##\ ##

Yeah sorry, it supposed to be 0.35 x (multiply) (1-0.04).

So, 1 - 0.04 is 0.96 which is more likely to happen than 0.04? Is that correct?

Why did we used 1 instead of any numbers? Is that part of the formula? It mentioned something like binary variable, something like that.
 
  • #4
One step at atime ?

0.60 x 0.04/0.60 x 0.04 + 0.35 * (1 - 0.04) = 0.3376

But we are getting there ...

:wink: sorrry to be so obnoxious ...


##\ ##
 
  • #5
BvU said:
One step at atime ?

0.60 x 0.04/0.60 x 0.04 + 0.35 * (1 - 0.04) = 0.3376

But we are getting there ...

:wink: sorrry to be so obnoxious ...


##\ ##

Ok, one step at a time. I am still confused why we should use 1 but not other number. Especially the 0.35*(1 - 0.04).
 
  • #6
What I mean is: Do not forget the brackets !

0.60 x 0.04/(0.60 x 0.04 + 0.35 * (1 - 0.04)) = ...

Bayes:
$$P(A|B) = {P(B|A)\,P(A)\over P(B)}$$compare that to the much more legible
$$P(A|B)={0.60\times 0.04\over 0.60\times 0.04+0.35\times (1-0.04)}$$
knowing
  • 35% of the companies that did not increase their share price by more than 5% in the same period replaced their CEOs
  • 60% of the companies that increased their share price by more than 5% in the last three years replaced their CEOs
In short: what is ##{ 0.60\times 0.04+0.35\times (1-0.04)}## ?


BvU said:
1-0.04 is the probability that the stock prices do NOT grow by more than 5%
We use the number 1 for a probability that is a certainty (100%).
For example $$P(A)+P(\neg A)=1$$ (##\neg A ## means NOT ##A## )

So in the denominator we expect to see ##P(B)## appearing: the probability the ceo is replaced

##\ ##
 
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  • #7
wonderingchicken said:
Ok, one step at a time. I am still confused why we should use 1 but not other number. Especially the 0.35*(1 - 0.04).
But it is much easier to begin answering your question if it is correctly stated. A computer (and many people) will take your equation literally. It doesn't cost you anything to put parenthesis around the entire denominator.
0.60 * 0.04/(0.60 * 0.04 + 0.35 * (1 - 0.04))
 
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  • #8
BvU said:
What I mean is: Do not forget the brackets !

0.60 x 0.04/(0.60 x 0.04 + 0.35 * (1 - 0.04)) = ...

Bayes:
$$P(A|B) = {P(B|A)\,P(A)\over P(B)}$$compare that to the much more legible
$$P(A|B)={0.60\times 0.04\over 0.60\times 0.04+0.35\times (1-0.04)}$$
knowing
  • 35% of the companies that did not increase their share price by more than 5% in the same period replaced their CEOs
  • 60% of the companies that increased their share price by more than 5% in the last three years replaced their CEOs
In short: what is ##{ 0.60\times 0.04+0.35\times (1-0.04)}## ?



We use the number 1 for a probability that is a certainty (100%).
For example $$P(A)+P(\neg A)=1$$ (##\neg A ## means NOT ##A## )

So in the denominator we expect to see ##P(B)## appearing: the probability the ceo is replaced

##\ ##

Oh, so the first P (B| A) P (A) is did increase their share by 5% and the second P (B| A) P (A) is did NOT increase their share by 5%, correct? If NOT, we have to subtract 1 with the first P(A)?
 
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  • #9
Yes. Spelling it out:

We are after
  • ##P(A|B)##, the probability that the shares of a company that fires its CEO will increase by more than 5%
knowing
  • ##P(B|A) = 0.60##, the probability of the CEO replacement given the stock price has increased by 5%
  • ##P(A)=0.04##, the probability that the stock prices grow by more than 5%
and Bayes' formula tells us
$$P(A|B) = {P(B|A)\,P(A)\over P(B)}$$so all we still need is
  • ##P(B)##, the probability that the CEO is replaced
For this last one we use$$P(B) = P(B|A)\,P(A)+P(B|\neg A)\,P(\neg A)$$
knowing
  • ##P(B|\neg A) = 0.35##, 35% of the companies that did not increase their share price by more than 5% replaced their CEOs
  • ##P(\neg A) = 1-0.04##, the probability that the stock prices do not grow by more than 5%
making use of the fact that stock prices either grow more than 5% or not grow more than 5%:

##\ ##
 
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  • #10
BvU said:
Yes. Spelling it out:

We are after
  • ##P(A|B)##, the probability that the shares of a company that fires its CEO will increase by more than 5%
knowing
  • ##P(B|A) = 0.60##, the probability of the CEO replacement given the stock price has increased by 5%
  • ##P(A)=0.04##, the probability that the stock prices grow by more than 5%
and Bayes' formula tells us
$$P(A|B) = {P(B|A)\,P(A)\over P(B)}$$so all we still need is
  • ##P(B)##, the probability that the CEO is replaced
For this last one we use$$P(B) = P(B|A)\,P(A)+P(B|\neg A)\,P(\neg A)$$
knowing
  • ##P(B|\neg A) = 0.35##, 35% of the companies that did not increase their share price by more than 5% replaced their CEOs
  • ##P(\neg A) = 1-0.04##, the probability that the stock prices do not grow by more than 5%
making use of the fact that stock prices either grow more than 5% or not grow more than 5%:

##\ ##
Thank you so much, Sir.
 
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FAQ: Why did it suddenly become subtractive? (Example of Bayes’ Theorem)

What does it mean for something to become subtractive in the context of Bayes' Theorem?

In the context of Bayes' Theorem, something becomes subtractive when the probability of an event decreases due to new evidence. This typically happens when the new evidence contradicts or diminishes the likelihood of the event occurring, leading to a reduction in the posterior probability.

How does Bayes' Theorem handle new evidence that is subtractive?

Bayes' Theorem updates the probability of a hypothesis based on new evidence. If the new evidence is subtractive, it means that the likelihood of the evidence given the hypothesis (P(E|H)) is low, which in turn reduces the posterior probability of the hypothesis. The formula adjusts by multiplying the prior probability by this lower likelihood and normalizing it with the evidence's overall probability.

Can you give an example where Bayes' Theorem results in a subtractive update?

Sure! Imagine a medical test for a disease where the prior probability (P(Disease)) is 5%. If a new test result comes back negative and the test is known to be highly accurate, the likelihood of getting a negative result if the person has the disease (P(Negative|Disease)) is very low. This new evidence will reduce the posterior probability of having the disease, making it a subtractive update.

Why is understanding subtractive updates important in Bayesian analysis?

Understanding subtractive updates is crucial because it allows for more accurate and dynamic modeling of probabilities based on new evidence. This is particularly important in fields like medicine, finance, and machine learning, where decisions must be updated continuously as new information becomes available.

How can subtractive updates impact decision-making processes?

Subtractive updates can significantly impact decision-making by lowering the confidence in a hypothesis or outcome. For instance, in a medical diagnosis, a subtractive update might lead to discontinuing a treatment that is no longer deemed necessary. In finance, it could mean revising investment strategies based on new market data. The ability to adjust decisions based on subtractive updates ensures more accurate and reliable outcomes.

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