Search results
Results From The WOW.Com Content Network
In probability theory, the coupon collector's problem refers to mathematical analysis of "collect all coupons and win" contests. It asks the following question: if each box of a given product (e.g., breakfast cereals) contains a coupon, and there are n different types of coupons, what is the probability that more than t boxes need to be bought ...
Conditional expectation. In probability theory, the conditional expectation, conditional expected value, or conditional mean of a random variable is its expected value evaluated with respect to the conditional probability distribution. If the random variable can take on only a finite number of values, the "conditions" are that the variable can ...
The conditional distribution contrasts with the marginal distribution of a random variable, which is its distribution without reference to the value of the other variable. If the conditional distribution of Y {\displaystyle Y} given X {\displaystyle X} is a continuous distribution , then its probability density function is known as the ...
Given n coupons, how many coupons do you expect you need to draw with replacement before having drawn each coupon at least once. —Preceding unsigned comment added by 131.155.59.211 11:30, 21 January 2010 (UTC) I agree with this. The problem defn. on the page was not clear to me at all. Magicmike 19:42, 22 February 2011 (UTC)
Probability theory. In probability theory, conditional probability is a measure of the probability of an event occurring, given that another event (by assumption, presumption, assertion or evidence) is already known to have occurred. [ 1] This particular method relies on event A occurring with some sort of relationship with another event B.
You can find instant answers on our AOL Mail help page. Should you need additional assistance we have experts available around the clock at 800-730-2563. Should you need additional assistance we have experts available around the clock at 800-730-2563.
This is because, in measure theory, the value of the Lebesgue integral of X is defined via weighted averages of approximations of X which take on finitely many values. [19] Moreover, if given a random variable with finitely or countably many possible values, the Lebesgue theory of expectation is identical to the summation formulas given above.
The violet is the mutual information . In information theory, the conditional entropy quantifies the amount of information needed to describe the outcome of a random variable given that the value of another random variable is known. Here, information is measured in shannons, nats, or hartleys. The entropy of conditioned on is written as .