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Leer Challenge 2: Bayes' Theorem | Statistics
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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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Sectie 6. Hoofdstuk 2

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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.

Taak

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.

Oplossing

Switch to desktopSchakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties
Was alles duidelijk?

Hoe kunnen we het verbeteren?

Bedankt voor je feedback!

Sectie 6. Hoofdstuk 2
Switch to desktopSchakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties
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