Conditional Dependent Probability

In summary, the conversation discusses a scenario where three players are drawing balls from a set with a certain number of white and black balls. The goal is to determine the chances of each player winning, assuming that black balls are replaced after being drawn. The conversation further includes a probability tree and a calculation of the probabilities for each player. The final ratio of their chances is 9:6:4, with player A having the highest chance of winning. The conversation ends with the speaker expressing gratitude and wanting to rate the response as "awesome."
  • #1
lilcoley23@ho
19
0
My questions I am looking at is:

We have twelve balls, four of which are white and eight are black. Three blindfolded players, A, B, and C draw a ball in turn, first A, then B, then C. The winner is the one who first draws a white ball. Assuming that each black ball is replaced after being drawn, find the ratio of the chances of each player.

I do not have any background on dependent proability with replacement, only without.

When making a probability tree with replacement would it look like this:

...PlayerA....Player B...Player C
...- ......- ......-
...- - - ...... - - -......- - -
...- - - -......- - - - ....- - - -
...b...w......b...w...b...w
...8/12...4/12....8/13...4/13...8/14...4/14

Please ignore the dots, it's the only way I could get my probability tree to look right.

My logic is that player A has an advantage because he's going first. So to reduce player 2's chances I added one to the sample set to symbolize that a turn had already been taken. Am I right in my thinking?
 
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  • #2
For the first round, A has a P of 1/3, B has a P of (2/3)1/3, while C has a P of (2/3)21/3. For each subsequent round, the ratio of their chances are the same. Thus their probabilities remain in the ratio 9:6:4.
So P(A)=9/19, P(B)=6/19, P(C)=4/19.
 
  • #3
Thank you so much! That makes so much more sense then what I was trying to do! Is there a way for me to rate your response as AWESOME!
 

FAQ: Conditional Dependent Probability

What is conditional dependent probability?

Conditional dependent probability is a type of probability that takes into account the relationship between two events. It is the likelihood that one event will occur, given that another event has already occurred.

How is conditional dependent probability calculated?

Conditional dependent probability is calculated by dividing the probability of the joint occurrence of two events by the probability of the first event. This can be represented as P(A|B) = P(A∩B) / P(B).

What is the difference between conditional dependent probability and unconditional probability?

The main difference between these two types of probability is that conditional dependent probability takes into account the occurrence of another event, while unconditional probability does not. Conditional dependent probability is also calculated differently, as it involves dividing by the probability of the first event.

What are some real-life examples of conditional dependent probability?

One example is the probability of getting a red card in a deck of cards, given that the first card drawn was a heart. Another example is the probability of getting into a car accident, given that it is raining outside.

How is conditional dependent probability used in scientific research?

In scientific research, conditional dependent probability is used to analyze the relationship between two variables. It allows researchers to determine the likelihood of one event occurring based on the occurrence of another event, and can help identify patterns and correlations in data.

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