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Proceedings ArticleDOI
Stephen M. Chu, Thomas S. Huang 
13 May 2002
7 Citations
The DBNs generalize the hidden Markov models by representing the hidden states as state variables, and allow the states to have complex interdependencies.
Proceedings ArticleDOI
24 Oct 1999
13 Citations
To our knowledge, this is the first publication which reports about the usage of pseudo 3-D hidden Markov models.
The results obtained are really promising, showing the wide applicability of the Hidden Markov Models methodology.
We show that our theorem is applicable to a wide class of hidden Markov models.
Journal ArticleDOI
W. H. Laverty, M. J. Miket, I. W. Kelly 
01 Mar 2002-The Statistician
15 Citations
This can be a very valuable aid in the understanding of hidden Markov models.
Open accessProceedings ArticleDOI
05 Jul 2008
34 Citations
It is shown to be more accurate than a standard hidden Markov model in this domain.
This article describes a new algorithm for exact computation of the observed information matrix in hidden Markov models that may be performed in a single pass through the data.
It extends previous work on homogeneous Markov chains to more general and applicable hidden Markov models.

Related Questions

How can hidden Markov models be used to classify volcanic signals?5 answersHidden Markov Models (HMMs) can be used to classify volcanic signals by capturing the characteristics of different types of volcanic events. HMMs have been successfully applied in the automated classification of seismic volcanic signals. They can model the underlying physical processes driving volcanic activity and extract important features from the seismic signals. HMMs have been used to classify seismic signals in near real-time, providing timely information for volcanic surveillance and monitoring. The incorporation of duration modeling in HMMs has shown to reduce false positive rates in event detection and improve accuracy. HMM-based classification schemes have also been enriched with discriminative steps, such as generative embedding, to enhance classification performance. Overall, HMMs offer a cost-effective and versatile approach for classifying volcanic signals and can be applied to various geophysics problems.
How can the hidden Markov model be used for fault diagnosis in rotary machinery?5 answersHidden Markov models (HMMs) can be used for fault diagnosis in rotary machinery. HMMs have been applied to fault detection and prediction in continuous industrial processes. However, the EM algorithm in HMMs can have local optimality problems. To address this, a hidden Markov model-Bayesian network (HMM-BN) hybrid model has been proposed. The model combines expert empirical knowledge with the EM algorithm to accurately diagnose the fault root cause variable. The improved EM algorithm uses sequential and parallel learning to alleviate initial sensitivity and local optimum problems. The model provides fault detection, prediction, and root cause variable detection results based on log-likelihood estimates calculated by the improved HMM. Experimental results using the Tennessee Eastman (TE) process and the continuously stirred tank reactor (CSTR) process demonstrate the feasibility and effectiveness of the model in finding faults and identifying their causes.
How can we build a hidden Markov model for a given sequence?5 answersA hidden Markov model (HMM) can be built for a given sequence by following a few steps. First, the observations obtained from the sequence need to be determined, even if they are incomplete or noisy. Then, the transition probabilities between the hidden states and the observations need to be estimated using an Expectation-Maximization algorithm. This algorithm helps in estimating the probabilities for the initial joint hidden-state-observation distribution as well. Once these probabilities are estimated, a belief state or filter recursion can be used to track the hidden states based on the calculations from the Expectation-Maximization algorithm. Additionally, a dynamic programming analog to the Viterbi algorithm can be employed to estimate the most likely sequence of hidden states given the sequence of observations. By following these steps, a hidden Markov model can be constructed for a given sequence.
How can the Hidden Markov Model be used in computer vision?5 answersHidden Markov Models (HMMs) are widely used in computer vision for various applications. One application is in recognizing human motion, such as artistic performances like Peking opera, where the poses are more complex. In this case, a Gaussian Mixture based HMM (GMM-HMM) is used for motion recognition, along with a method to filter abnormal and missing data. HMMs are also used in proximity capacitive sensors to detect user gestures, where the models are trained using probability concepts to obtain correct input signal probability feedback. In visual inspection, HMMs are applied to analyze fixations' sequences during the inspection of front panels, allowing for the differentiation between expert and novice operators. Additionally, HMMs are used in hand gesture recognition, where they are combined with trajectory code words and other techniques to achieve high accuracy and real-time performance.
How to solve hidden Markov model?7 answers
Is Hidden Markov model machine learning?7 answers

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Are hmms more sensitive than direct sequence mappings?
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Profile Hidden Markov Models (pHMMs) are indeed more sensitive than direct sequence mappings. pHMMs are powerful tools for modeling biological sequence diversity and detecting divergent sequences with high sensitivity. They outperform direct sequence mappings, especially in low-identity regions below 20%, as pHMMs derived from structural alignments show significantly better performance in recognizing remote homologies compared to those derived from sequence alignments. Additionally, pHMMs are crucial for protein homology detection, providing a probabilistic framework that allows for the identification of homologous protein sequences within the same protein family, even when sequences are highly divergent. This enhanced sensitivity of pHMMs is further supported by the improved performance shown in detecting sequence families within genomes compared to standard profile HMMs or Blast searches.
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Measuring SAR at different sequences of brain activation holds significant potential in neuroscience research. By utilizing SAR measurements, researchers can assess the power absorbed by the human brain during various brain activities. This data can help in studying the biological effects of electromagnetic fields on the central nervous system. Additionally, SAR measurements can aid in understanding the reactivation of task-related neural codes during rest, which is crucial for supporting cognitive functions. Furthermore, SAR measurements can be integrated with functional MRI (fMRI) brain mapping to analyze structured random processes observed through multiple event sequences, enhancing the robustness to noise and variability of active fMRI signals. Overall, measuring SAR at different brain activation sequences offers insights into brain function, electromagnetic field effects, and cognitive processes in neuroscience research.
How do two groups who don't know each other communicate?
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Two groups who do not know each other can communicate collectively through distant communication methods, where interactions are group-based rather than individual-based. Intergroup communication involves various linguistic strategies such as category labels, language abstraction, and metaphors, which play a crucial role in conveying social meanings and establishing stereotypes. Lack of prior intergroup contact experience can lead to miscommunications regarding relationship interest, potentially hindering cross-group friendship formation. In the context of a communication network, hidden groups may attempt to conceal their communications within the network, requiring probabilistic approaches like Hidden Markov models for detection without relying on semantic information. Overall, distant communication, linguistic strategies, prior contact experiences, and hidden group detection methods all influence how two unfamiliar groups can effectively communicate.
Overview of maize production in Nigeria?
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Maize production in Nigeria is influenced by various factors. Studies show that the country's maize output interacts with climatic elements like rainfall and temperature, with the Hidden Markov Model (HMM) outperforming the Long Short-Term Memory neural network in forecasting yields. Disparities exist in the maize production value chain, with male farmers having larger land holdings, better access to inputs like fertilizers, and higher productivity compared to female farmers. Organic maize farming faces challenges such as inadequate accreditation agencies, high costs of organic seeds, and poor marketing systems, highlighting the need for support mechanisms like financial assistance and training to enhance participation. Small-scale maize producers in Anambra State benefit from factors like planting hybrid maize, proper fertilizer use, and early planting to increase profitability and ensure a high return on investment.
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