Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  addresses the problem of unsupervised Bayesian hidden Markov chain restoration. When the hidden chain is stationary, the classical "Hidden Markov Chain" (HMC) model is quite efficient, and associated unsupervised Bayesian restoration methods using the "Expectation-Maximization" (EM) algorithm work ll. When the hidden chain is non stationary, on the other hand, the unsupervised restoration results using the HMC model can be poor, due to a bad match beten the real and estimated models. The novelty of this paper is to offer a more appropriate model for hidden nonstationary Markov chains, via the theory of evidence. Using recent results relating to Triplet Markov Chains (TMCs),  show, via simulations, that the classical restoration results can be improved by the use of the theory of evidence and Dempster-Shafer fusion. The latter improvement is performed in an entirely unsupervised way using an original parameter estimation method. Some application examples to unsupervised image segmentation are also provided.
['Pierre Lanchantin', 'Wojciech Pieczynski']
I am writing a research paper on a idea Unsupervised restoration of hidden nonstationary Markov chains using evidential priors
[{"from": "human", "value": "I am writing a research paper on a idea Unsupervised restoration of hidden nonstationary Markov chains using evidential priors"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  addresses the problem of unsupervised Bayesian hidden Markov chain restoration. When the hidden chain is stationary, the classical \"Hidden Markov Chain\" (HMC) model is quite efficient, and associated unsupervised Bayesian restoration methods using the \"Expectation-Maximization\" (EM) algorithm work ll. When the hidden chain is non stationary, on the other hand, the unsupervised restoration results using the HMC model can be poor, due to a bad match beten the real and estimated models. The novelty of this paper is to offer a more appropriate model for hidden nonstationary Markov chains, via the theory of evidence. Using recent results relating to Triplet Markov Chains (TMCs),  show, via simulations, that the classical restoration results can be improved by the use of the theory of evidence and Dempster-Shafer fusion. The latter improvement is performed in an entirely unsupervised way using an original parameter estimation method. Some application examples to unsupervised image segmentation are also provided."}]