Law of total probability
In probability theory, the law 'of total probability' is a fundamental rule relating marginal probabilities to conditional probabilities. It expresses the total probability of an outcome which can be realized via several distinct events, hence the name.
Statement
The law of total probability is a theorem that states, in its discrete case, if is a finite or countably infinite set of mutually exclusive and collectively exhaustive events, then for any eventor, alternatively,
where, for any, if, then these terms are simply omitted from the summation since is finite.
The summation can be interpreted as a weighted average, and consequently the marginal probability,, is sometimes called "average probability"; "overall probability" is sometimes used in less formal writings.
The law of total probability can also be stated for conditional probabilities:
Taking the as above, and assuming is an event independent of any of the :
Continuous case
The law of total probability extends to the case of conditioning on events generated by continuous random variables. Let be a probability space. Suppose is a random variable with distribution function, and an event on. Then the law of total probability statesIf admits a density function, then the result is
Moreover, for the specific case where, where is a Borel set, then this yields
Example
Suppose that two factories supply light bulbs to the market. Factory XApplying the law of total probability, we have:
where
- is the probability that the purchased bulb was manufactured by factory X;
- is the probability that the purchased bulb was manufactured by factory Y;
- is the probability that a bulb manufactured by X will work for over 5000 hours;
- is the probability that a bulb manufactured by Y will work for over 5000 hours.