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  2. Expected value - Wikipedia

    en.wikipedia.org/wiki/Expected_value

    According to this definition, E[X] exists and is finite if and only if E[X +] and E[X −] are both finite. Due to the formula |X| = X + + X −, this is the case if and only if E|X| is finite, and this is equivalent to the absolute convergence conditions in the definitions above. As such, the present considerations do not define finite ...

  3. Secretary problem - Wikipedia

    en.wikipedia.org/wiki/Secretary_problem

    Taking the derivative of P(x) with respect to , setting it to 0, and solving for x, we find that the optimal x is equal to 1/e. Thus, the optimal cutoff tends to n/e as n increases, and the best applicant is selected with probability 1/e. For small values of n, the optimal r can also be obtained by standard dynamic programming methods.

  4. Random walk - Wikipedia

    en.wikipedia.org/wiki/Random_walk

    Five eight-step random walks from a central point. Some paths appear shorter than eight steps where the route has doubled back on itself. (animated version)In mathematics, a random walk, sometimes known as a drunkard's walk, is a stochastic process that describes a path that consists of a succession of random steps on some mathematical space.

  5. Rule of succession - Wikipedia

    en.wikipedia.org/wiki/Rule_of_succession

    In probability theory, the rule of succession is a formula introduced in the 18th century by Pierre-Simon Laplace in the course of treating the sunrise problem. [1] The formula is still used, particularly to estimate underlying probabilities when there are few observations or events that have not been observed to occur at all in (finite) sample data.

  6. Probability density function - Wikipedia

    en.wikipedia.org/wiki/Probability_density_function

    Unlike a probability, a probability density function can take on values greater than one; for example, the continuous uniform distribution on the interval [0, 1/2] has probability density f(x) = 2 for 0 ≤ x1/2 and f(x) = 0 elsewhere.

  7. Normalizing constant - Wikipedia

    en.wikipedia.org/wiki/Normalizing_constant

    Bayes' theorem says that the posterior probability measure is proportional to the product of the prior probability measure and the likelihood function. Proportional to implies that one must multiply or divide by a normalizing constant to assign measure 1 to the whole space, i.e., to get a probability measure.

  8. Washington State QB John Mateer to enter transfer portal ...

    www.aol.com/washington-state-qb-john-mateer...

    There's a new No. 1-ranked player in the transfer portal. Washington State quarterback John Mateer is entering the transfer portal, Cougars coach Jake Dickert confirmed Monday.

  9. Conditional expectation - Wikipedia

    en.wikipedia.org/wiki/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 only take on a subset of ...