Bayes' Theorem for Employee Napping Probability

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In summary, using Bayes' Theorem and assuming a uniform distribution of nap starting times, the probability of an employee starting their nap before 1 pm if their boss finds them napping during their shift is 0.73. However, this solution may not be accurate if the employee takes multiple naps during their shift or if the nap starts after 3 pm.
  • #1
temaire
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Homework Statement


An employee goes to work from 9 am to 4 pm. He takes a nap for an average of 2 hours if he starts napping before 1 pm and naps for an average of 1 hours if he starts napping after 1 pm. His boss randomly checks up on him once during his shift. If his boss finds him napping, what is the probability that he starts napping before 1 pm?

Homework Equations


Bayes' Theorem:
[tex]P(A_1|B)=\frac{P(A_1)P(B|A_1)}{P(B|A_1)P(A_1)+P(B|A_2)P(A_2)}[/tex]

The Attempt at a Solution


Event A1: Employee naps before 1 pm
Event A2: Employee naps after 1 pm
Event B: Boss finds employee napping

[tex]P(A_1)=\frac{4}{7}[/tex]
[tex]P(A_2)=\frac{3}{7}[/tex]
[tex]P(B|A_1)=\frac{2}{7}[/tex]
[tex]P(B|A_2)=\frac{1}{7}[/tex]

[tex]P(A_1|B)=\frac{P(A_1)P(B|A_1)}{P(B|A_1)P(A_1)+P(B|A_2)P(A_2)}[/tex]
[tex]P(A_1|B)=\frac{\frac{4}{7}\cdot \frac{2}{7}}{\frac{2}{7}\cdot \frac{4}{7}+\frac{1}{7}\cdot \frac{3}{7}}[/tex]
[tex]P(A_1|B)=\frac{8}{11}=0.73[/tex]

Is my solution correct?
 
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  • #2
Insufficient information.

You assumed that the starting time of the nap is random with a uniform distribution.
a) That should be specified in the problem.
b) That can lead to problems if the nap starts after 3 PM.

If we use that assumption of a uniform nap starting time and ignore the potential issue with 4 pm, your answer is right.
 
  • #3
Thanks for the response.

I made the assumption that the naps could be broken up into multiple segments and that the total nap times were 2 hours or 1 hour. This avoids the problem of the employee taking an hour long nap right before his shift ends.
 

What is Bayes' Theorem?

Bayes' Theorem is a mathematical formula that allows us to update our beliefs about the probability of an event occurring based on new information or evidence.

How is Bayes' Theorem used in science?

Bayes' Theorem is used in science to make predictions and draw conclusions based on available data. It is commonly used in fields such as statistics, biology, and artificial intelligence.

What are the key components of Bayes' Theorem?

The key components of Bayes' Theorem are the prior probability, the likelihood of the evidence, and the posterior probability. The prior probability is our initial belief about the probability of an event occurring. The likelihood of the evidence is the probability of the evidence occurring if the event is true. The posterior probability is the updated belief about the probability of the event occurring after considering the new evidence.

What is the difference between Bayes' Theorem and conditional probability?

Conditional probability is a measure of the likelihood of an event occurring given that another event has already occurred. Bayes' Theorem, on the other hand, allows us to update our beliefs about the probability of an event occurring based on new evidence.

What are some real-world applications of Bayes' Theorem?

Bayes' Theorem is used in a variety of real-world applications, such as medical diagnosis, spam filtering, and weather forecasting. It is also used in decision-making processes, such as in business and finance.

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