# 은닉 마르코프 모델

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## 노트

• Hidden Markov models are used in speech recognition.[1]
• Build an HMM for each word using the associated training set.[1]
• Now the Markov process is not hidden at all and the HMM is just a Markov chain.[1]
• This section describes HMMs with a simple categorical model for outputs $$y_t \in \{ 1, \dotsc, V \}$$.[2]
• This is a marginalization problem, and for HMMs, it is computed with the so-called forward algorithm.[2]
• With the package mHMMbayes you can fit multilevel hidden Markov models.[3]
• With the package mHMMbayes , one can estimate these multilevel hidden Markov models.[3]
• This tutorial starts out with a brief description of the HMM and the multilevel HMM.[3]
• For a more elaborate and gentle introduction to HMMs, we refer to Zucchini, MacDonald, and Langrock (2016).[3]
• We describe how such methods are applied to these generalized hidden Markov models.[4]
• We conclude this review with a discussion of Bayesian methods for model selection in generalized HMMs.[4]
• Calculation of the parameters of Hidden Markov models used in the navigation systems of surface transportation for map matching: A review.[5]
• Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning.[5]
• Hidden markov model approaches for biological studies.[6]
• The probabilistic model to characterize a hidden Markov process is referred to as a hidden Markov model (abbreviated as HMM).[6]
• In what follows the first-order HMM is used to illustrate the theory.[6]
• The principle of trellis algorithm is extensively used in statistical analysis for 1-D hidden Markov models.[6]
• In addition, we demonstrate that our HMM can detect transitions in neural activity corresponding to targets not found in training data.[7]
• In this work, we describe the process of design and parameter learning for a hidden Markov model (HMM) representing goal-directed movements.[7]
• In addition to a model of state transitions, an HMM is specified by the way the latent state variable can be observed.[7]
• Figure 2A depicts this simple HMM, with each circle representing an HMM state and single arrows representing allowed state transitions.[7]
• From an HMM, individual stochastic rate constants can be calculated using Eq.[8]
• In other words, the parameters of the HMM are known.[9]
• The diagram below shows the general architecture of an instantiated HMM.[9]
• The task is usually to derive the maximum likelihood estimate of the parameters of the HMM given the set of output sequences.[9]
• Hidden Markov models can also be generalized to allow continuous state spaces.[9]
• In addition, due to the inter-dependencies among difficulty choices, we apply a hidden Markov model (HMM).[10]
• We add to the literature an application of the HMM approach in characterizing test takers' behavior in self-adapted tests.[10]
• Using HMM we obtained the transition probabilities between the latent classes.[10]
• We then report the results of the HMM analysis addressing specifically the two research questions.[10]
• Recognizing human action in time-sequential images using hidden Markov model.[11]
• Classical music composition using hidden Markov models.[11]
• On the application of vector quantization and hidden Markov models to speaker-independent, isolated word recognition.[11]
• Speaker independent isolated digit recognition using hidden Markov models.[11]
• Statistical models called hidden Markov models are a recurring theme in computational biology.[12]
• Hidden Markov models (HMMs) are a formal foundation for making probabilistic models of linear sequence 'labeling' problems1,2.[12]
• Starting from this information, we can draw an HMM (Fig. 1).[12]
• It's useful to imagine an HMM generating a sequence.[12]
• As a first example, we apply the HMM to calculate the probability that we feel cold for two consecutive days.[13]
• A similar approach to the one above can be used for parameter learning of the HMM model.[13]
• We have some dataset, and we want to find the parameters which fit the HMM model best.[13]
• Then based on Markov and HMM assumptions we follow the steps in figures Fig.6, Fig.7.[14]
• Kyle Kastner built HMM class that takes in 3d arrays, I’m using hmmlearn which only allows 2d arrays.[15]
• An HMM is a mixture model consisting of two components: an observable time series and an underlying latent state sequence.[16]
• The two components of an HMM with their dependence structure are visualised in Fig.[16]
• To illustrate how the likelihood function is constructed for a two-state HMM consider again the t.p.m.[16]
• To fit an HMM to our data, we assume that the 44 samples are independent and that the model parameters are identical across all sessions.[16]
• Rabiner L.R. A tutorial on hidden Markov models and selected applications in speech recognition.[17]
• The evaluation of the likelihood of HMMs has been made practical by an algorithm called the forward-backward procedure.[17]
• The second section briefly describes the computation of likelihood and estimation of HMM parameters through use of the standard algorithms.[17]
• During the training phase an HMM is “taught” the statistical makeup of the observation strings for its dedicated word.[18]
• Then, for each HMM, the question is asked: How likely (in some sense) is it that this HMM produced this incoming observation string?[18]
• The word associated with the HMM of highest likelihood is declared to be the recognized word.[18]
• Note carefully that it is not the purpose of an HMM to generate observation strings.[18]
• The harmonic HMM provides a model on the basis of which statistics can be derived that quantify an individual's rest–activity rhythm.[19]
• Then we present the details of training a single HMM in Section 2.3.[20]
• The MHMM combining multiple vessel features with multiple HMMs is given in Section 2.4.[20]
• The proposed MHMM is the combination of multidimensional HMMs.[20]
• One HMM ( ) can be expressed as a five item array as , where is the number of invisible tissue states.[20]

## 메타데이터

### Spacy 패턴 목록

• [{'LOWER': 'hidden'}, {'LOWER': 'markov'}, {'LEMMA': 'model'}]
• [{'LEMMA': 'HMM'}]