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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. 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.

  4. 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 ...

  5. Craps - Wikipedia

    en.wikipedia.org/wiki/Craps

    The probability of dice combinations determine the odds of the payout. There are a total of 36 (6 × 6) possible combinations when rolling two dice. The following chart shows the dice combinations needed to roll each number. The two and twelve are the hardest to roll since only one combination of dice is possible.

  6. Pig (dice game) - Wikipedia

    en.wikipedia.org/wiki/Pig_(dice_game)

    The game of Pig is played with a single six-sided die. Pig is a simple die game first described in print by John Scarne in 1945. [1] Players take turns to roll a single die as many times as they wish, adding all roll results to a running total, but losing their gained score for the turn if they roll a .

  7. 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 ...