Bayes theorem and probability help

In summary, the probability of a driver needing to be concerned if the warning light goes on is 11.7%. This can be calculated using the law of total probability and Bayes' theorem, taking into account the different probabilities for the warning light flashing in different scenarios. This includes a 99% chance of the light flashing when the oil pressure is too low, a 2% chance of it flashing for no reason, and a 10% chance of the oil pressure actually being low.
  • #1
hoddo
5
0

Homework Statement


'A dashboard warning light is supposed to flash red if a car’s oil pressure is
too low. On a certain model, the probability of the light flashing when it should is 0.99; 2%
of the time, though, it flashes for no apparent reason. If there is a 10% chance that the oil
really is low, what is the probability that a driver needs to be concerned if the warning light
goes on?'


Homework Equations



Law of total probability/bayes theorem?


The Attempt at a Solution


having trouble making a start..
 
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  • #2


Perhaps try drawing a tree diagram to help represent the situation.
 
  • #3


i'm not sure how to do that though,
because of the 0.99 and the 2%,
add up to more than 1?
 
  • #4


They are for different cases though, which is why it adds up to more than 100%.

The question states that IF the oil pressure is actually too low, then it will flash 99% of the time.

On the other hand though, if the oil pressure is not too low, then it will still flash 2% of the time.

Can you see how those figures are allowed to add up to more than 100%? Does that help?
 
  • #5


P(concerned) = (0.99 x 0.1) + (0.02 x 0.9) = 0.117?
 

Related to Bayes theorem and probability help

What is Bayes theorem?

Bayes theorem, also known as Bayes' rule or Bayes' law, is a mathematical formula that describes the relationship between conditional and marginal probabilities of two events. It helps to calculate the probability of an event occurring based on prior knowledge or information.

What is the significance of Bayes theorem in probability?

Bayes theorem is an important tool in probability theory as it allows us to update our beliefs about the likelihood of an event occurring based on new evidence. It helps to incorporate new information into our existing knowledge and make more accurate predictions.

How is Bayes theorem used in real-world applications?

Bayes theorem is applied in a wide range of fields, including statistics, machine learning, and artificial intelligence. It is used to make predictions, classify data, and solve problems in various industries such as healthcare, finance, and marketing.

What are the assumptions of Bayes theorem?

Bayes theorem assumes that the events being considered are independent, the prior probability is known, and the sample size is large enough to accurately represent the population. It also assumes that the prior probability is updated as new evidence is obtained.

How can one calculate the posterior probability using Bayes theorem?

The posterior probability can be calculated by multiplying the prior probability by the likelihood and dividing it by the sum of the prior probabilities for all possible outcomes. This gives the probability of the event occurring after taking into account new evidence.

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