X-Nico

7 unusual facts about expected value


Expected linear time MST algorithm

The expected number of F-light edges in G is at most n/p where n is the number of vertices in G

The root has m edges so the expected number of edges is equal to 2m plus twice the expected number of edges in each right subproblem.

If a parent problem has x edges then the expected number of edges in the left child problem is at most x/2.

Feature hashing

When a second hash function ξ is used to determine the sign of a feature's value, the expected mean of each column in the output array becomes zero because ξ causes some collisions to cancel out.

Gumption trap

The "trap" portion of the term refers to the negative feedback loop that the event or mindset creates: That the reduction in the person's enthusiasm and initiative decreases both the person's likelihood of success in that project and the degree of success likely (thus doubly affecting the expected outcome of the person's efforts).

Huffman coding

;Find: A prefix-free binary code (a set of codewords) with minimum expected codeword length (equivalently, a tree with minimum weighted path length from the root).

Nicola Acocella

This offers a novel contribution to the analysis of conditions not only for policy effectiveness or neutrality (showing the limits of validity of many currently accepted propositions on the effects of rational expectations and time inconsistency as well as on the role of policy announcements), but also for existence, uniqueness or multiplicity of the equilibrium in strategic games.


Vysochanskij–Petunin inequality

In probability theory, the Vysochanskij–Petunin inequality gives a lower bound for the probability that a random variable with finite variance lies within a certain number of standard deviations of the variable's mean, or equivalently an upper bound for the probability that it lies further away.


see also