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Challenge 2: Bayes' Theorem

In the world of probability and statistics, Bayesian thinking offers a framework for understanding the probability of an event based on prior knowledge. It contrasts with the frequentist approach, which determines probabilities based on the long-run frequencies of events. Bayes' theorem is a fundamental tool within this Bayesian framework, connecting prior probabilities and observed data.

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Imagine you are a data scientist working for a medical diagnostics company. Your company has developed a new test for a rare disease. The prevalence of this disease in the general population is 1%. The test has a 99% true positive rate (sensitivity) and a 98% true negative rate (specificity).

Your task is to compute the probability that a person who tests positive actually has the disease.

Given:

  • P(Disease) = Probability of having the disease = 0.01
  • P(Positive|Disease) = Probability of testing positive given that you have the disease = 0.99
  • P(Negative|No\ Disease) = Probability of testing negative given that you don't have the disease = 0.98

Using Bayes' theorem:

P(Disease|Positive) = P(Positive|Disease) * P(Disease) / P(Positive)

Where P(Positive) can be found using the law of total probability:

P(Positive) = P(Positive|Disease) * P(Disease) + P(Positive|No Disease) * P(No Disease)

Compute P(Disease|Positive), the probability that a person who tests positive actually has the disease.

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Challenge 2: Bayes' Theorem

In the world of probability and statistics, Bayesian thinking offers a framework for understanding the probability of an event based on prior knowledge. It contrasts with the frequentist approach, which determines probabilities based on the long-run frequencies of events. Bayes' theorem is a fundamental tool within this Bayesian framework, connecting prior probabilities and observed data.

Opgave

Swipe to start coding

Imagine you are a data scientist working for a medical diagnostics company. Your company has developed a new test for a rare disease. The prevalence of this disease in the general population is 1%. The test has a 99% true positive rate (sensitivity) and a 98% true negative rate (specificity).

Your task is to compute the probability that a person who tests positive actually has the disease.

Given:

  • P(Disease) = Probability of having the disease = 0.01
  • P(Positive|Disease) = Probability of testing positive given that you have the disease = 0.99
  • P(Negative|No\ Disease) = Probability of testing negative given that you don't have the disease = 0.98

Using Bayes' theorem:

P(Disease|Positive) = P(Positive|Disease) * P(Disease) / P(Positive)

Where P(Positive) can be found using the law of total probability:

P(Positive) = P(Positive|Disease) * P(Disease) + P(Positive|No Disease) * P(No Disease)

Compute P(Disease|Positive), the probability that a person who tests positive actually has the disease.

Løsning

Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

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Completion rate improved to 2.33

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