The hidden Markov model, which is a statistical Markov model, was developed by L. E. Baum and coworkers. Information about it can be found either online on websites like Wikipedia or in books about mathematics.
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It's not the Markov algorithm, it is the Luhn Algorithm.
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Unlike MM in HMM state is hidden.
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Qiang Miao has written:
'Application of wavelets and hidden Markov model in condition-based maintenance'
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The variable "hn" is typically used to represent the new state in a Hidden Markov Model. This state depends on the previous state and the transition probabilities. The new state "hn" is calculated based on a combination of the previous state and the transition probabilities in the model.
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Brian Marcus has written:
'Entropy of hidden Markov processes and connections to dynamical systems' -- subject(s): Dynamics, Entropy (Information theory), Congresses, Markov processes
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Matthew Stephen Ryan has written:
'Dynamic character recognition using Hidden Markov Models'
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Aleksandr Markov's birth name is Aleksandr Vladimirovitch Markov.
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Andrei Markov's birth name is Andrei Viktorovich Markov.
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Evgeniy Markov's birth name is Yevgeniy Lvovitch Markov.
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Leonid Markov's birth name is Markov, Leonid Vasilyevich.
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An artificial neural network is a structure which will attempt to find a relationship i.e. a function between the inputs, and the provided output(s), in order that when the net be provided with unseen inputs, and according with the recorded internal data (named "weights"), will try to find a correct answer for the new inputs. Hidden Markov models, are used for find the states for which a given stochastic process went through. The main difference could be this: In order to use a markov chain, the process must depend only in it´s last state. For use a neural network, you need a lot of past data. After training process, neural networks are capable of predicting next states of the system based only on the last state. In addition, given the ability to measure the prediction error (for example, after actual event, signal or state has happend and was compared to prediction), the neural network is capable of adapting itself and capture online changes in the undergoing process to improve the model of prediction and decrease the estimation error for the next states. Theoretically such approach can eliminate the need in initial training, as the network started from some random model will eventually adapt itself to the actual process it tries to estimate given this feedback error loop and will start to make correct estimations / predictions after a certain amount of steps. In such setup one can assume that neural network can be used when no past data is available at all. In this case neural network build the model of the ongoing process "from scratch" based on the observations in the "online" mode.
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Andrey Markov is notable mathematician known for proving mathematical theories like Markov Brothers' Inequality, Markov chains and Markov processes. Some of his other contributions are solving linear differential equations, constructive mathematics and recursive functions theory.
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Georgi Markov has written:
'Georgi Markov v \\' -- subject(s): Politics and government, Sports, History
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