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  2. Gambler's ruin - Wikipedia

    en.wikipedia.org/wiki/Gambler's_ruin

    In statistics, gambler's ruin is the fact that a gambler playing a game with negative expected value will eventually go bankrupt, regardless of their betting system.. The concept was initially stated: A persistent gambler who raises his bet to a fixed fraction of the gambler's bankroll after a win, but does not reduce it after a loss, will eventually and inevitably go broke, even if each bet ...

  3. Risk of ruin - Wikipedia

    en.wikipedia.org/wiki/Risk_of_ruin

    Risk of ruin is a concept in gambling, insurance, and finance relating to the likelihood of losing all one's investment capital or extinguishing one's bankroll below the minimum for further play. [1] For instance, if someone bets all their money on a simple coin toss, the risk of ruin is 50%.

  4. Markov chain - Wikipedia

    en.wikipedia.org/wiki/Markov_chain

    If the Markov chain is time-homogeneous, then the transition matrix P is the same after each step, so the k-step transition probability can be computed as the k-th power of the transition matrix, P k. If the Markov chain is irreducible and aperiodic, then there is a unique stationary distribution π. [41]

  5. List of statistics articles - Wikipedia

    en.wikipedia.org/wiki/List_of_statistics_articles

    Download as PDF; Printable version; ... Absorbing Markov chain; ABX test; Accelerated failure time model; ... Gambler's fallacy; Gambler's ruin;

  6. Gambling and information theory - Wikipedia

    en.wikipedia.org/wiki/Gambling_and_information...

    When these constraints apply (as they invariably do in real life), another important gambling concept comes into play: in a game with negative expected value, the gambler (or unscrupulous investor) must face a certain probability of ultimate ruin, which is known as the gambler's ruin scenario. Note that even food, clothing, and shelter can be ...

  7. Stochastic process - Wikipedia

    en.wikipedia.org/wiki/Stochastic_process

    The Brownian motion process and the Poisson process (in one dimension) are both examples of Markov processes [193] in continuous time, while random walks on the integers and the gambler's ruin problem are examples of Markov processes in discrete time. [194] [195]

  8. Optional stopping theorem - Wikipedia

    en.wikipedia.org/wiki/Optional_stopping_theorem

    Then the gambler's fortune over time is a martingale, and the time τ at which he decides to quit (or goes broke and is forced to quit) is a stopping time. So the theorem says that E[X τ] = E[X 0]. In other words, the gambler leaves with the same amount of money on average as when he started. (The same result holds if the gambler, instead of ...

  9. Examples of Markov chains - Wikipedia

    en.wikipedia.org/wiki/Examples_of_Markov_chains

    A game of snakes and ladders or any other game whose moves are determined entirely by dice is a Markov chain, indeed, an absorbing Markov chain. This is in contrast to card games such as blackjack, where the cards represent a 'memory' of the past moves. To see the difference, consider the probability for a certain event in the game.