Where is hidden Markov model is used?
Answers from top 8 papers
More filters
Papers (8) | Insight |
---|---|
To our knowledge, this is the first publication which reports about the usage of pseudo 3-D hidden Markov models. | |
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