Featured
- Get link
- Other Apps
Bayesian statistics
Bayesian statistics is a branch of statistics that
interprets probability as a measure of belief or confidence in an event
occurring. This approach is named after Thomas Bayes, who formulated Bayes’
theorem. Here’s a simple breakdown:
- Prior
Probability: This represents your initial belief about the probability
of an event before seeing any new data.
- Likelihood:
This is the probability of observing the new data given your initial
belief.
- Posterior
Probability: This is the updated probability of the event after taking
the new data into account.
Bayes’ theorem mathematically combines these elements to
update your beliefs based on new evidence. This method is particularly
useful in fields where you continuously gather new data and need to update your
predictions or beliefs accordingly12
Example
Let’s go
through a simple example of Bayesian statistics.
Imagine you
have a medical test for a rare disease. The disease affects 1 in 1,000 people
(0.1% prevalence). The test is 99% accurate, meaning it correctly identifies
99% of those with the disease (true positive rate) and correctly identifies 99%
of those without the disease (true negative rate).
Now, suppose
you take the test and it comes back positive. What is the probability that you
actually have the disease?
Here’s how
we can use Bayes’ theorem to find out:
- Prior Probability (P(Disease)):
The initial probability of having the disease is 0.1% or 0.001.
- Likelihood (P(Test Positive |
Disease)): The probability of testing positive if you have the disease is
99% or 0.99.
- Probability of Test Positive
(P(Test Positive)): This includes both true positives and false positives.
It can be calculated as:
- P(Test Positive) = P(Test
Positive | Disease) * P(Disease) + P(Test Positive | No Disease) * P(No
Disease)
- P(Test Positive) = (0.99 *
0.001) + (0.01 * 0.999) = 0.001 + 0.00999 = 0.01099
- Posterior Probability (P(Disease
| Test Positive)): The updated probability of having the disease given a
positive test result is:
- P(Disease | Test Positive) =
(P(Test Positive | Disease) * P(Disease)) / P(Test Positive)
- P(Disease | Test Positive) =
(0.99 * 0.001) / 0.01099 ≈ 0.090
So, even
with a positive test result, the probability that you actually have the disease
is about 9%.
This example
shows how Bayesian statistics can help update our beliefs based on new
evidence. Does this make sense? Would you like to explore another example or a
different concept?
- Get link
- Other Apps
Popular Posts
- Get link
- Other Apps
- Get link
- Other Apps
Comments
Post a Comment