Calculating Conditional Probabilities with Mutually Exclusive Events

In summary, the formula for evaluating P(A|(B or C)) is not as simple as P(A|B) + P(A|C) as long as B and C are mutually exclusive. Instead, the formula is P(A|B ∪ C) = P(A|B) + P(A|C) - P(A|B ∩ C), and there is no simple general way to split it into fractions with denominators P(B) and P(C). This can be seen in the example of drawing a card from a standard deck where the events C (card is a Club) and H (card is a Heart) are mutually exclusive, but the formula does not simplify to P(A|C) + P(A|
  • #1
quantumnano
4
0
How would I evaluate the following:
P(A|(B or C)) where B and C are two mutually exclusive events.

I have scoured a couple texts and the internet but have made no headway, any insight would be greatly appreciated.

QN
 
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  • #2
My guess: P(A|B or C) = P(A|B) + P(A|C) as long as B and C are mutually exclusive.
 
  • #3
mathman said:
My guess: P(A|B or C) = P(A|B) + P(A|C) as long as B and C are mutually exclusive.

No. Consider drawing one card from a standard deck.

[tex]\begin{align*}
A & = \text{ card is an Ace}\\
C & = \text{ card is a Club}\\
H & = \text{ card is a Heart}
\end{align*}
[/tex]

Then [tex] C, H [/tex] are mutually exclusive. Now

[tex]
\begin{align*}
P(A \mid C) & = \frac 1 {13} \\
P(A \mid H) & = \frac 1 {13} \\
P(A \mid C) + P(A \mid H) & = \frac{2}{13} \\
P(A \mid C \cup H) & = \frac{2}{26} = \frac 1{13}
\end{align*}
[/tex]

The problem is this (for general events)

[tex]
P(A \mid B \cup C) = \frac{P(A \cap (B \cup C))}{P(B \cup C)} = \frac{P(A \cap B)}{P(B) + P(C)} + \frac{P(A \cap C)}{P(B) + P(C)} \tag{1}
[/tex]

and there is no simple general way to split (1) into a sum of different fractions where one has denominator [tex] P(B) [/tex] and the other has denominator [tex] P(C) [/tex].
 

Related to Calculating Conditional Probabilities with Mutually Exclusive Events

1. What is conditional probability?

Conditional probability is a mathematical concept that refers to the likelihood of an event occurring given that another event has already occurred. It is expressed as P(A|B), where A and B are two events, and it represents the probability of event A occurring given that event B has already occurred.

2. How is conditional probability calculated?

The formula for calculating conditional probability is P(A|B) = P(A and B) / P(B), where P(A and B) is the probability of both events A and B occurring, and P(B) is the probability of event B occurring.

3. What is the difference between conditional probability and joint probability?

Conditional probability refers to the probability of an event occurring given that another event has already occurred, while joint probability refers to the probability of two or more events occurring at the same time. In other words, conditional probability is a subset of joint probability.

4. How is conditional probability used in real life?

Conditional probability is used in various fields, including insurance, finance, and medicine, to make predictions and decisions based on available data. For example, insurance companies use conditional probability to calculate premiums based on the likelihood of a certain event occurring given the customer's age, health, and other factors.

5. Can conditional probability be greater than 1?

No, conditional probability cannot be greater than 1. This is because the probability of an event occurring cannot be greater than the probability of both events occurring together, which is the denominator in the formula for conditional probability. If the calculated conditional probability is greater than 1, it is most likely due to an error in the input data or the formula used.

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