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  2. 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] The problem was: Which of the following three propositions has the greatest chance of success?

  3. 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 ...

  4. Discrete uniform distribution - Wikipedia

    en.wikipedia.org/wiki/Discrete_uniform_distribution

    In probability theory and statistics, the discrete uniform distribution is a symmetric probability distribution wherein each of some finite whole number n of outcome values are equally likely to be observed. Thus every one of the n outcome values has equal probability 1/n. Intuitively, a discrete uniform distribution is "a known, finite number ...

  5. Dice notation - Wikipedia

    en.wikipedia.org/wiki/Dice_notation

    For instance, 4d6−L means a roll of 4 six-sided dice, dropping the lowest result. This application skews the probability curve towards the higher numbers, as a result a roll of 3 can only occur when all four dice come up 1 (probability ⁠ 1 / 1,296 ⁠), while a roll of 18 results if any three dice are 6 (probability ⁠ 21 / 1,296 ...

  6. Bernoulli trial - Wikipedia

    en.wikipedia.org/wiki/Bernoulli_trial

    Graphs of probability P of not observing independent events each of probability p after n Bernoulli trials vs np for various p.Three examples are shown: Blue curve: Throwing a 6-sided die 6 times gives a 33.5% chance that 6 (or any other given number) never turns up; it can be observed that as n increases, the probability of a 1/n-chance event never appearing after n tries rapidly converges to 0.

  7. Examples of Markov chains - Wikipedia

    en.wikipedia.org/wiki/Examples_of_Markov_chains

    To see the difference, consider the probability for a certain event in the game. In the above-mentioned dice games, the only thing that matters is the current state of the board. The next state of the board depends on the current state, and the next roll of the dice. It does not depend on how things got to their current state.

  8. Gambler's fallacy - Wikipedia

    en.wikipedia.org/wiki/Gambler's_fallacy

    For a fair 16-sided die, the probability of each outcome occurring is ⁠ 1 / 16 ⁠ (6.25%). If a win is defined as rolling a 1, the probability of a 1 occurring at least once in 16 rolls is: [] = % The probability of a loss on the first roll is ⁠ 15 / 16 ⁠ (93.75%). According to the fallacy, the player should have a higher chance of ...

  9. Dice pool - Wikipedia

    en.wikipedia.org/wiki/Dice_pool

    In many RPG systems, non-trivial actions often require dice rolls. Some RPGs roll a fixed number of dice, add a number to the die roll based on the character's attributes and skills, and compare the resulting number with a difficulty rating. In other systems, the character's attributes and skills determine the number of dice to be rolled.