Conditional Probability

In summary, the probability of Romeo receiving a reply from Juliet on Monday is 0.25 and on Tuesday is 0.35. Using Bayes' Theorem, the probability of receiving a reply on Tuesday, given that he did not receive one on Monday, is 0.583.
  • #1
brandy
161
0

Homework Statement


romeo proposed to juliet. now he's waiting for her response.
R = 'event that she replies'
W='event that she wants to get married'
Mon = 'event on monday'
Tue = 'event on Tuesday'

P(R[itex]\wedge[/itex]Mon | W) = 0.2
P(R[itex]\wedge[/itex]Tue | W) = 0.25
P(R[itex]\wedge[/itex]Mon| [itex]\bar{W}[/itex]) = 0.05
P(R[itex]\wedge[/itex]Tue | [itex]\bar{W}[/itex]) = 0.1
P(R|W) = 1.0
P(R|[itex]\bar{W}[/itex]) = 0.7
P(W)=0.6

If Romeo has not received her reply on Monday, what is the probability that he will receive the letter on Tuesday?

Homework Equations


there are more probabilities for each day of the week for both W and bar W.


The Attempt at a Solution



I used to total probability to calculate P(R [itex]\wedge[/itex] Mon) = 0.25, and for tuesday = 0.35
and i believe what I am trying to calculate now is P(R[itex]\wedge[/itex] Tue | [itex]\bar{Mon}[/itex]) [itex]\wedge[/itex] W)

so far, because its too difficult to latex it all. i have applied bayes theorem, and i have tried fiddling around with all 4 of the given ones. I need some direction.
 
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  • #2
brandy said:

Homework Statement


romeo proposed to juliet. now he's waiting for her response.
R = 'event that she replies'
W='event that she wants to get married'
Mon = 'event on monday'
Tue = 'event on Tuesday'

P(R[itex]\wedge[/itex]Mon | W) = 0.2
P(R[itex]\wedge[/itex]Tue | W) = 0.25
P(R[itex]\wedge[/itex]Mon| [itex]\bar{W}[/itex]) = 0.05
P(R[itex]\wedge[/itex]Tue | [itex]\bar{W}[/itex]) = 0.1
P(R|W) = 1.0
P(R|[itex]\bar{W}[/itex]) = 0.7
P(W)=0.6

If Romeo has not received her reply on Monday, what is the probability that he will receive the letter on Tuesday?

Homework Equations


there are more probabilities for each day of the week for both W and bar W.


The Attempt at a Solution



I used to total probability to calculate P(R [itex]\wedge[/itex] Mon) = 0.25, and for tuesday = 0.35
and i believe what I am trying to calculate now is P(R[itex]\wedge[/itex] Tue | [itex]\bar{Mon}[/itex]) [itex]\wedge[/itex] W)

so far, because its too difficult to latex it all. i have applied bayes theorem, and i have tried fiddling around with all 4 of the given ones. I need some direction.

What formulas did you use to get P{Mon & R} = 0.25, etc.? I get very different results.

RGV
 
  • #3
i just did P(R∧Mon | W) + P(R∧Mon| Wˉ) = 0.2+0.05=0.25
so, this isn't right?
 
  • #4
brandy said:
i just did P(R∧Mon | W) + P(R∧Mon| Wˉ) = 0.2+0.05=0.25
so, this isn't right?

No. Go back and look in detail at Bayes' Theorem.

RGV
 

What is Conditional Probability?

Conditional Probability is a mathematical concept that measures the likelihood of an event occurring given that another event has already occurred. Essentially, it is the probability of an event happening under a specific condition.

How is Conditional Probability calculated?

Conditional Probability is calculated by dividing the probability of the two events happening together by the probability of the condition being met. This can be represented as P(A|B) = P(A and B) / P(B), where P(A|B) is the conditional probability of event A given event B has already occurred.

What is the difference between Conditional Probability and Joint Probability?

Conditional Probability and Joint Probability are similar concepts, but they have a key difference. Conditional Probability measures the probability of an event occurring given that another event has already occurred, while Joint Probability measures the probability of two events happening together.

How is Conditional Probability used in real life?

Conditional Probability is used in many real-life situations, such as weather forecasting, medical diagnoses, and financial risk analysis. It can also be used in decision-making processes, such as predicting the outcome of a sports game or determining the likelihood of success for a new product launch.

What are some common misconceptions about Conditional Probability?

One common misconception is that the probability of two events happening together is always equal to the product of their individual probabilities. In reality, this is only true for independent events, and for dependent events, the calculation must account for the conditional probability. Another misconception is that the condition must be a cause of the event, when in fact it can simply be related or correlated in some way.

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