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Graph of number of coupons, n vs the expected number of trials (i.e., time) needed to collect them all, E (T ) In probability theory, the coupon collector's problem refers to mathematical analysis of "collect all coupons and win" contests.
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 .
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 condition number is derived from the theory of propagation of uncertainty, and is formally defined as the value of the asymptotic worst-case relative change in output for a relative change in input. The "function" is the solution of a problem and the "arguments" are the data in the problem. The condition number is frequently applied to ...
Conditional variance. In probability theory and statistics, a conditional variance is the variance of a random variable given the value (s) of one or more other variables. Particularly in econometrics, the conditional variance is also known as the scedastic function or skedastic function. [ 1] Conditional variances are important parts of ...
Hyperbolic discounting is mathematically described as. where g ( D) is the discount factor that multiplies the value of the reward, D is the delay in the reward, and k is a parameter governing the degree of discounting (for example, the interest rate ). This is compared with the formula for exponential discounting:
Neumann boundary condition. In mathematics, the Neumann (or second-type) boundary condition is a type of boundary condition, named after Carl Neumann. [ 1] When imposed on an ordinary or a partial differential equation, the condition specifies the values of the derivative applied at the boundary of the domain .
It represents an outcome of (=) whenever a value x of X is observed. The conditional probability of A given X can thus be treated as a random variable Y with outcomes in the interval [,]. From the law of total probability, its expected value is equal to the unconditional probability of A.