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The "Markov" in "Markov decision process" refers to the underlying structure of state transitions that still follow the Markov property. The process is called a "decision process" because it involves making decisions that influence these state transitions, extending the concept of a Markov chain into the realm of decision-making under uncertainty.
A Tolerant Markov model (TMM) is a probabilistic-algorithmic Markov chain model. [6] It assigns the probabilities according to a conditioning context that considers the last symbol, from the sequence to occur, as the most probable instead of the true occurring symbol. A TMM can model three different natures: substitutions, additions or deletions.
In probability theory and statistics, a Markov chain or Markov process is a stochastic process describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event.
This category is for articles about the theory of Markov chains and processes, and associated processes. See Category:Markov models for models for specific applications that make use of Markov processes.
The term Markov assumption is used to describe a model where the Markov property is assumed to hold, such as a hidden Markov model. A Markov random field extends this property to two or more dimensions or to random variables defined for an interconnected network of items. [1] An example of a model for such a field is the Ising model. A discrete ...
In this framework, each decision influences subsequent choices and system outcomes, taking into account the current state, available actions, and the probabilistic nature of state transitions. [1] This process is used for modeling and regulation of dynamic systems , especially under uncertainty, and is commonly addressed using methods like ...
A POMDP models an agent decision process in which it is assumed that the system dynamics are determined by an MDP, but the agent cannot directly observe the underlying state. Instead, it must maintain a sensor model (the probability distribution of different observations given the underlying state) and the underlying MDP. Unlike the policy ...
The model appears in Ronald A. Howard's book. [3] The models are often studied in the context of Markov decision processes where a decision strategy can impact the rewards received. The Markov Reward Model Checker tool can be used to numerically compute transient and stationary properties of Markov reward models.