# 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]}

### 소스

- ↑
^{1.0}^{1.1}^{1.2}Hidden Markov Models - ↑
^{2.0}^{2.1}Stan User’s Guide - ↑
^{3.0}^{3.1}^{3.2}^{3.3}Multilevel HMM tutorial - ↑
^{4.0}^{4.1}AN INTRODUCTION TO HIDDEN MARKOV MODELS AND BAYESIAN NETWORKS - ↑
^{5.0}^{5.1}A Hidden Markov Model-Based Map-Matching Algorithm for Wheelchair Navigation - ↑
^{6.0}^{6.1}^{6.2}^{6.3}Hidden markov model approaches for biological studies - ↑
^{7.0}^{7.1}^{7.2}^{7.3}Detecting Neural-State Transitions Using Hidden Markov Models for Motor Cortical Prostheses - ↑ Hidden Markov Model - an overview
- ↑
^{9.0}^{9.1}^{9.2}^{9.3}Hidden Markov model - ↑
^{10.0}^{10.1}^{10.2}^{10.3}Understanding Test Takers' Choices in a Self-Adapted Test: A Hidden Markov Modeling of Process Data - ↑
^{11.0}^{11.1}^{11.2}^{11.3}A Systematic Review of Hidden Markov Models and Their Applications - ↑
^{12.0}^{12.1}^{12.2}^{12.3}What is a hidden Markov model? - ↑
^{13.0}^{13.1}^{13.2}Introduction to Hidden Markov Models - ↑ Markov and Hidden Markov Model
- ↑ Hidden Markov Model
- ↑
^{16.0}^{16.1}^{16.2}^{16.3}Modelling reassurances of clinicians with hidden Markov models - ↑
^{17.0}^{17.1}^{17.2}Applying Hidden Markov Models to the Analysis of Single Ion Channel Activity - ↑
^{18.0}^{18.1}^{18.2}^{18.3}Hidden Markov Models - an overview - ↑ Hidden Markov models for monitoring circadian rhythmicity in telemetric activity data
- ↑
^{20.0}^{20.1}^{20.2}^{20.3}Multiple Hidden Markov Model for Pathological Vessel Segmentation

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### 위키데이터

- ID : Q176769