In game theory, a Markov strategy[1] is one that depends only on state variables that summarize the history of the game in one way or another.[2] For instance, a state variable can be the current play in a repeated game, or it can be any interpretation of a recent sequence of play.
A profile of Markov strategies is a Markov perfect equilibrium if it is a Nash equilibrium in every state of the game. The Markov strategy was invented by Andrey Markov.[3]
^"First Links in the Markov Chain". American Scientist. 2017-02-06. Retrieved 2017-02-06.
^Fudenberg, Drew (1995). Game Theory. Cambridge, MA: The MIT Press. pp. 501–40. ISBN 0-262-06141-4.
^Sack, Harald (2022-06-14). "Andrey Markov and the Markov Chains". SciHi Blog. Retrieved 2017-11-23.
In game theory, a Markovstrategy is one that depends only on state variables that summarize the history of the game in one way or another. For instance...
A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on...
A hidden Markov model (HMM) is a Markov model in which the observations are dependent on a latent (or "hidden") Markov process (referred to as X {\displaystyle...
Markov perfect equilibrium is a set of mixed strategies for each of the players which satisfy the following criteria: The strategies have the Markov property...
the standard assumptions, the switching strategy has a 2/3 probability of winning the car, while the strategy of keeping the initial choice has only a...
Solving chess consists of finding an optimal strategy for the game of chess; that is, one by which one of the players (White or Black) can always force...
the other player of a game has a winning strategy, and the consequences of the existence of such strategies. Alternatively and similarly, "determinacy"...
theory, a Markov reward model or Markov reward process is a stochastic process which extends either a Markov chain or continuous-time Markov chain by adding...
In the mathematical theory of probability, an absorbing Markov chain is a Markov chain in which every state can reach an absorbing state. An absorbing...
alternatively be obtained by considering limited information strategies. A Markovstrategy is one that only uses the most recent move of the opponent and...
non-determined topological games. A strategy for P is stationary if it depends only on the last move by P's opponent; a strategy is Markov if it depends both on the...
The layered hidden Markov model (LHMM) is a statistical model derived from the hidden Markov model (HMM). A layered hidden Markov model (LHMM) consists...
In computer science, an evolution strategy (ES) is an optimization technique based on ideas of evolution. It belongs to the general class of evolutionary...
In game theory, a stochastic game (or Markov game), introduced by Lloyd Shapley in the early 1950s, is a repeated game with probabilistic transitions played...
in all other iterations). Altman, Eitan; Hayel, Yezekael (July 2010). "Markov Decision Evolutionary Games". IEEE Transactions on Automatic Control. 55...
random walk Markov chain Examples of Markov chains Detailed balance Markov property Hidden Markov model Maximum-entropy Markov model Markov chain mixing...
_{G}} on Ω {\displaystyle \Omega } . The discriminator's strategy set is the set of Markov kernels μ D : Ω → P [ 0 , 1 ] {\displaystyle \mu _{D}:\Omega...
Karlis, Dimitris (2023). "Football tracking data: a copula-based hidden Markov model for classification of tactics in football". Annals of Operations Research...
time according to such rules is modeled as a Markov chain with a state variable such as the current strategy profile or how the game has been played in...
bioinformatics Margin Markov chain geostatistics Markov chain Monte Carlo (MCMC) Markov information source Markov logic network Markov model Markov random field...
A local search strategy makes incremental changes aimed at improving the score of the structure. A global search algorithm like Markov chain Monte Carlo...
parameterized, mathematicians often use a Markov chain Monte Carlo (MCMC) sampler. The central idea is to design a judicious Markov chain model with a prescribed...
process Markov information source Markov kernel Markov logic network Markov model Markov network Markov process Markov property Markov random field Markov renewal...
or a combination of both. There have also been other algorithms based on Markov chains. In 2012, researchers from the Ishikawa Watanabe Laboratory at the...
Global Information