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Hidden Markov model; Hidden Markov random field; Hidden semi-Markov model; Hierarchical Bayes model; Hierarchical clustering; Hierarchical hidden Markov model; Hierarchical linear modeling; High-dimensional statistics; Higher-order factor analysis; Higher-order statistics; Hirschman uncertainty; Histogram; Historiometry; History of randomness ...
In current research, it is common to use a Markov chain to model how once a country reaches a specific level of economic development, the configuration of structural factors, such as size of the middle class, the ratio of urban to rural residence, the rate of political mobilization, etc., will generate a higher probability of transitioning from ...
In the mathematical theory of stochastic processes, variable-order Markov (VOM) models are an important class of models that extend the well known Markov chain models. In contrast to the Markov chain models, where each random variable in a sequence with a Markov property depends on a fixed number of random variables, in VOM models this number of conditioning random variables may vary based on ...
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.
[1] [2] Such models are often described as M/G/1 type Markov chains because they can describe transitions in an M/G/1 queue. [3] [4] The method is a more complicated version of the matrix geometric method and is the classical solution method for M/G/1 chains. [5]
A family of Markov chains is said to be rapidly mixing if the mixing time is a polynomial function of some size parameter of the Markov chain, and slowly mixing otherwise. This book is about finite Markov chains, their stationary distributions and mixing times, and methods for determining whether Markov chains are rapidly or slowly mixing. [1] [4]
In statistics, Markov chain Monte Carlo (MCMC) is a class of algorithms used to draw samples from a probability distribution. Given a probability distribution, one can construct a Markov chain whose elements' distribution approximates it – that is, the Markov chain's equilibrium distribution matches the target distribution. The more steps ...
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.