# Bayes's theorem in examples

## Table of Contents

## Definition

Bayes' theorem is a formula that describes how to update the probabilities of hypotheses when given evidence. It follows simply from the axioms of conditional probability, but can be used to powerfully reason about a wide range of problems involving belief updates.

Given a hypothesis **H** and evidence **E**, Bayes' theorem states that
the relationship between the probability of the hypothesis **P(H)**
before getting the evidence and the probability **P(H|E)** of the
hypothesis after getting the evidence is:

\(P(H|E) = \frac{P(E|H)}{P(E)}P(H)\)

## Explanation

## Examples

### Brest cancer detection

1% of women at age forty who participate in routine screening have breast cancer. 80% of women with breast cancer will get positive mammographies. 9.6% of women without breast cancer will also get positive mammographies. A woman in this age group had a positive mammography in a routine screening. What is the probability that she actually has breast cancer?

### Explanation

The correct answer is 7.8%, obtained as follows: Out of 10,000 women, 100 have breast cancer; 80 of those 100 have positive mammographies. From the same 10,000 women, 9,900 will not have breast cancer and of those 9,900 women, 950 will also get positive mammographies. This makes the total number of women with positive mammographies 950+80 or 1,030. Of those 1,030 women with positive mammographies, 80 will have cancer. Expressed as a proportion, this is 80/1,030 or 0.07767 or 7.8%.

To put it another way, before the mammography screening, the 10,000 women can be divided into two groups:

- Group 1: 100 women with breast cancer.
- Group 2: 9,900 women without breast cancer.

Summing these two groups gives a total of 10,000 patients, confirming that none have been lost in the math. After the mammography, the women can be divided into four groups:

- Group A: 80 women with breast cancer, and a positive mammography.
- Group B: 20 women with breast cancer, and a negative mammography.
- Group C: 950 women without breast cancer, and a positive mammography.
- Group D: 8,950 women without breast cancer, and a negative mammography.

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