[Statistics] Conditional Probability questions?

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Homework Help Overview

The discussion revolves around conditional probability, specifically applying Bayes' Theorem to two problems. The first problem involves understanding the components of the theorem, particularly the term P(A|B'), while the second problem raises questions about calculating joint probabilities and the relevance of given conditional information.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning, Problem interpretation

Approaches and Questions Raised

  • Participants are attempting to clarify the meaning of P(A|B') and its role in Bayes' Theorem. There is confusion about the application of the theorem and the calculation of P(AB) based on the provided information. Some participants are questioning the necessity of certain conditional probabilities in their calculations.

Discussion Status

The discussion is ongoing, with participants providing alternative formulations of Bayes' Theorem and addressing misunderstandings about the assumptions behind the calculations. There is no explicit consensus, but some guidance has been offered regarding the correct application of conditional probability.

Contextual Notes

Participants are working with specific problems from a textbook, which may impose constraints on the information available for solving the problems. There is mention of a diagram in the first problem that could be relevant to understanding the context.

KendrickLamar
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Homework Statement



I've attached both the problems into one image to make life easier since problem 1 has a diagram and the other does not.

SqHcA.jpg


Homework Equations


Bayes Theorem : P(A|B)P(B) / [P(A|B)P(B) + P(A|B')P(B')]
B' = B Complement
Z39bp.jpg

The Attempt at a Solution


well for the first one
i don't understand what the P(A|B') is and the .849 is that referring to B'?
SO i did .001(.05) / [(.001(.05)) + P(A|B')(.849)] i don't know what that P(A|B') is so I am not sure where to go from there and the answer in the back of the book says .0099

for the 2nd problem
a.) i understand don't need help
b.) i don't know how to get P(AB) given the information provided
c.) well i can get this if i know part b since it would be .4 + .25 - P(AB)

thank u for ur help!
 
Last edited:
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It's not clear where your brackets end in Bayes's Theorem. Try this instead:
P(B|A) = P(A|B) *(P(B))/(P(A))
 
^ that doesn't seem to work, like I'm still not clear on when I'm supposed to use baye's theorem?

ah sorry forgot the final bracket it says this:

Bayes Theorem : P(A|B)P(B) / [P(A|B)P(B) + P(A|B')P(B')]

in the textbook it says that ah well here let me just take a picture of it:
Z39bp.jpg
 
also i think the 2nd one the conditional information of .7 is unecessary and it should just be that

P(AB) = P(A)*P(B) = (.4)(.25) = .1?
 
When you say that P(AB) = P(A) * P(B), you're assuming that the two events are mutually exclusive. But since P(A|B) = 0.7, we know that's not true (otherwise P(A|B) = P(A)). What you want is the probability of A and B (i.e. their intersection).
 
Also, the version of Bayes' Theorem you're using is more complex than you need to solve the first problem. The basic formula for conditional probability is

P(B|A) = P(B \cap A)/P(A)

So you know you're in A because that's given. So you want to know, what's the probability that you'll be in the (tiny) part of A that overlaps with B?
 

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