Example 21: Speaks the truth 3 times in 4: reports a six,
A man is known to speak truth 3 out of 4 times. He throws a die and reports that it is a six. Find the probability that it is actually a six.
Let : the man reports that a six occurs, : six occurs, and : six does not occur.
and form a partition of the sample space (the die either shows a six or it does not).
and
= P(he speaks the truth)
= P(he does not speak the truth)
By the theorem of total probability,
By Bayes' theorem,
The probability that it is actually a six is .
Want the reason behind every step, the basics and the checks?
A report of "six" can be a true report or a lie. A true report needs a six (rare, ) and the truth (). A lie needs a non-six (common, ) and a lie (). Because non-sixes are five times as common, the lies are not much rarer than the true reports, and the report is less trustworthy than "3 times out of 4" suggests. Bayes' theorem gives the exact share of true reports.
- Given
- The man speaks the truth with probability . He throws a die and reports a six.
- To find
- The probability that the die actually shows six, given his report: .
- Plan
- (1) Name the events; check the partition. (2) Find and . (3) Total probability for . (4) Bayes' theorem.
Basics you need
A six comes up with probability , and not a six with probability . Basics: the classical definition
"Speaks the truth 3 out of 4 times" means he lies with probability . Basics: rules of probability
Solution
Name the events
Let
- : the man reports that a six occurs;
- : a six occurs on the die;
- : a six does not occur.
and are complements, so they form a partition of the sample space.
What the die really shows is the hidden cause; his report is what we observe. We want the chance of the cause "six" given the report.is a partition. Lesson: Bayes' theoremThe probabilities
If a six did occur, he reports a six exactly when he tells the truth: probability . If a six did not occur, a report of "six" is a lie. Like the book, we assume that his report is just "six" or "not six", so every lie about a non-six is the report "six" and is the whole chance of a lie, . This is an assumption of the model: if a lie could name any of the five wrong numbers equally, would be and the answer would be instead.The chance of the report: total probability
By the theorem of total probability,
The tree for Example 21. "Six" is reported after a real six and the truth (rose path, ), or after another number and a lie (green path, ). The lie path is the larger. The two ways of hearing "six" cannot both happen, so their probabilities add. Both products already have the denominator 24.Multiply along a path, add the paths. Basics: tree diagramsActually a six, given the report: Bayes' theorem
By Bayes' theorem,
The top is the true-report path, the bottom is every report of six; the 24 cancels. This is the book's , written with a common denominator.Bayes' theorem: one path over all the paths. Lesson: Bayes' theoremWhat the answer means
Before the report the chance of a six was ; after it, . The report helps, but it is still more likely () that the die is not a six.
A man who is right 3 times in 4 sounds reliable, but here he is reporting something rare. Five times as many throws are non-sixes, and a quarter of those turn into false reports of "six".
The probability that the die actually shows a six is .
(Textbook answer: .)
Picture 24 throws, each with a report. A six comes up about 4 times, and he truthfully reports it 3 of those times. A non-six comes up about 20 times, and he lies (reports a six) 5 of those times. So he says "six" times, and 3 of these are real: .
- Answering . That is , the chance that he reports a six when a six occurs, not the chance of a six given his report.
- Using for both branches. When no six occurs, a report of "six" is a lie, so its probability is .
- Forgetting the die. The prior matters; with a coin (prior ) the same truth-teller would give a different answer.
- For a witness or a test, the answer depends on both its reliability and how likely the reported thing was beforehand.
- The same question with a coin, where both outcomes are equally likely: Exercise 13.3, Question 13.
Every fact used, and where it comes from
| Fact | Used in | Source |
|---|---|---|
| An event and its complement form a partition | Step 1 | Section 13.5.1 (Lesson: Bayes' theorem) |
| Fair die; complement rule | Step 2 | Class XI, Probability (Basics: rules of probability) |
| Theorem of total probability | Step 3 | Section 13.5.2 (Lesson: total probability) |
| Bayes' theorem | Steps 4, 5 | Section 13.5 (Examples 15 to 21) |
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