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An MEMM is a discriminative model that extends a standard maximum entropy classifier by assuming that the unknown values to be learnt are connected in a Markov chain rather than being conditionally independent of each other.
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The Maxi- mum Entropy Markov Model is the extension of MaxEnt to the sequence labeling task, adding components such as the Viterbi algorithm. Although this ...
Maximum Entropy Design by a Markov Chain Process - Oxford Academic
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Jun 14, 2023 · In this article, we study an implementation of maximum entropy (ME) design utilizing a Markov chain. This design, which is also called the conditional Poisson ...
Dec 17, 2019 · The maximum entropy model is a conditional probability model p(y|x) that allows us to predict class labels given a set of features for a given ...
Output: A maximum-entropy-based Markov model that takes an unlabeled sequence of observations and predicts their corresponding labels.
While MaxEnt computes probabilities for each input independently, Markov chain recognizes that there's dependency from one state to the next.
Security of processes can be quantified by modeling them as Markovian models. Entropy and leakage of a Markov chain can be computed in polynomial time.
To find the maximum entropy Markov chain, we use the thermodynamic formalism, which provides insightful connections with statistical physics and thermodynamics ...
The Markov chain that has maximum entropy for given first and second moments is determined and provides a discrete analog to the continuous Gauss-Markov ...
This Markov chain, referred to as the maximal entropy random walk (MERW), possesses the property that all walks of equal length with given start and end node ...