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  2. Intransitive dice - Wikipedia

    en.wikipedia.org/wiki/Intransitive_dice

    The probability that A rolls a higher number than B, the probability that B rolls higher than C, and the probability that C rolls higher than A are all ⁠ 5 / 9 ⁠, so this set of dice is intransitive. In fact, it has the even stronger property that, for each die in the set, there is another die that rolls a higher number than it more than ...

  3. Newton–Pepys problem - Wikipedia

    en.wikipedia.org/wiki/Newton–Pepys_problem

    The Newton–Pepys problem is a probability problem concerning the probability of throwing sixes from a certain number of dice. [1]In 1693 Samuel Pepys and Isaac Newton corresponded over a problem posed to Pepys by a school teacher named John Smith. [2]

  4. Probability distribution - Wikipedia

    en.wikipedia.org/wiki/Probability_distribution

    A discrete probability distribution is the probability distribution of a random variable that can take on only a countable number of values [15] (almost surely) [16] which means that the probability of any event can be expressed as a (finite or countably infinite) sum: = (=), where is a countable set with () =.

  5. File:Dirichlet.pdf - Wikipedia

    en.wikipedia.org/wiki/File:Dirichlet.pdf

    You are free: to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made.

  6. Classical definition of probability - Wikipedia

    en.wikipedia.org/wiki/Classical_definition_of...

    The classical definition of probability assigns equal probabilities to events based on physical symmetry which is natural for coins, cards and dice. Some mathematicians object that the definition is circular. [11] The probability for a "fair" coin is... A "fair" coin is defined by a probability of... The definition is very limited.

  7. Multinomial distribution - Wikipedia

    en.wikipedia.org/wiki/Multinomial_distribution

    As increases, most of the probability mass is concentrated in a subset of , near , and the probability distribution near becomes well-approximated by (,,) (^) From this, we see that the subset upon which the mass is concentrated has radius on the order of /, but the points in the subset are separated by distance on the order of /, so at large ...

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