Markov model

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Markov model

A model used in decision analysis for evaluating potential outcomes of a disease process, which are defined as specific health states, transitions among which are modelled iteratively. In standard decision tree analysis, a patient moves through states—for example, from not treated, to treated, to final outcome; in a Markov process, a patient moves between states (e.g., backwards and forward between continuous ambulatory peritoneal dialysis and haemodialysis). Some states cannot be left once entered (so-called “absorbing states”), including death.

Markov chain, Markov model

a mathematical model that makes it possible to study complex systems by establishing a state of the system and then effecting a transition to a new state, such a transition being dependent only on the values of the current state, and not dependent on the previous history of the system up to that point.
References in periodicals archive ?
com visitors with the most sensitive, new bioinformatics methods such as hidden Markov models.
We employ a wide variety of computational systems, and GeneMatcher is particularly well-suited for the high-throughput application of hidden Markov models," said Dr.
Markov models have been used to understand a wide range of phenomena from weather forecasting, speech recognition and internet search.
Although only a single shared channel is considered in these Markov models, this motivates us to develop an analytical model based on Markov chain.
2013) compared different techniques of action recognition using Hidden Markov Models (HMM).
At that early stage, neural network based speech recognition was radically different from Hidden Markov Models (HMMs) based speech recognition.
Hidden Markov models (HMMs) are widely used to model the maneuvering process of an agent.
Stochastic process modeling methods such Markov Models and Petri nets are needed if one wants to model the time and sequence dependence on failures and repairs well in addition to the structure.
The Markov models have been used in health service decision making, including clinical and epidemiological application.
Later progress in error modeling introduced new mathematical concepts and model classes, often referred to as pure models: semi-Markov models, Hidden Markov Models (HMMs), empirical approaches including algorithmic models, chaos models, Deterministic Process Based Generative Models (DPBGM) and Stochastic Context-Free Grammar models (SCFG).
A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition.