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Hidden Markov Models with mixtures as emission distributions
2013
Statistics and computing
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In this paper, a semiparametric modeling where the emission distributions are a mixture of parametric distributions is proposed to get a higher flexibility. ...
In unsupervised classification, Hidden Markov Models (HMM) are used to account for a neighborhood structure between observations. ...
using mixture as emission distributions (Li, 2005 , Baudry et al., 2008 . ...
doi:10.1007/s11222-013-9383-7
fatcat:3xp2q3ymzjgchbyyshifi6tyvm
Hidden Markov Models with mixtures as emission distributions
[article]
2012
arXiv
pre-print
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In this paper, a semiparametric modeling where the emission distributions are a mixture of parametric distributions is proposed to get a higher flexibility. ...
In unsupervised classification, Hidden Markov Models (HMM) are used to account for a neighborhood structure between observations. ...
using mixture as emission distributions (Li, 2005 , Baudry et al., 2008 . ...
arXiv:1206.5102v1
fatcat:j7s5qpdpljd43kb42qooxsalr4
Finite state space non parametric Hidden Markov Models are in general identifiable
[article]
2013
arXiv
pre-print
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In this paper, we prove that finite state space non parametric hidden Markov models are identifiable as soon as the transition matrix of the latent Markov chain has full rank and the emission probability ...
distributions are linearly independent. ...
The emission distribution µ j is defined as µ j = ψ j φ . (4) A simple mixture model (i.e. when the hidden variable X i are iid) with such emission distribution is not identifiable (see [2] ). ...
arXiv:1306.4657v1
fatcat:b4s2h4dr4jdsro6giirvvxasim
MRHMMs: Multivariate Regression Hidden Markov Models and the variantS
2014
Computer applications in the biosciences : CABIOS
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First, MRHMMs provides a diverse set of emission probability structures, including mixture of multivariate normal distributions and (logistic) regression models. ...
Hidden Markov models (HMMs) are flexible and widely used in scientific studies. ...
MATERIALS AND METHODS An HMM can be viewed as a mixture model in which the latent (hidden) state has the Markov property. ...
doi:10.1093/bioinformatics/btu070
pmid:24558116
pmcid:PMC4058941
fatcat:cpeajlfryje7rn5vdtxqwkz4ly
Hhsmm: An R package for hidden hybrid Markov/semi-Markov models
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2109.12489v5 fatcat:ls6vrhatlbfmpdbtwdzraj4yb4Other Versions
The hhsmm also includes Markov/semi-Markov switching regression model as well as the auto-regressive HHSMM, the nonparametric estimation of the emission distribution using penalized B-splines, prediction ...
This paper introduces the hhsmm R package, which involves functions for initializing, fitting, and predication of hidden hybrid Markov/semi-Markov models. ...
Examples of hidden hybrid Markov/semi-Markov models Some examples of HHSMM models are as follows: • Models with macro-states: The macro-states are series or parallel networks of states with common emission ...
arXiv:2109.12489v6
fatcat:4faxrk7konaktp6nib4vzfc5iy
Effects of Frequency-Based Inter-frame Dependencies on Automatic Speech Recognition
[chapter]
2014
Lecture Notes in Computer Science
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Model A Hidden Markov Model with a Matrix Normal Mixture Model as the emission density was designed. Motivations High-dimensional features. ...
Motivations Signal processing theories show that the rate at which information changes in signals is proportional to frequency. 2 Model A Hidden Markov Model with a Matrix Normal Mixture Model as the emission ...
Model A Hidden Markov Model with a Matrix Normal Mixture Model as the emission density was designed. ...
doi:10.1007/978-3-319-06483-3_38
fatcat:rfknz3lchbbojlfaapn6gfol3q
Implementing spectral methods for hidden Markov models with real-valued emissions
[article]
2014
arXiv
pre-print
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Hidden Markov models (HMMs) are widely used statistical models for modeling sequential data. The parameter estimation for HMMs from time series data is an important learning problem. ...
Using experiments with synthetic data, the algorithms are compared with each other. ...
For the initial state h 1 , the initial state distribution is denoted by the vector π ∈ R k , with π j = Pr [h 1 = j] . (1.6)
Emissions The emissions of a hidden Markov model can be parameterized by ...
arXiv:1404.7472v1
fatcat:jlwnuj67vfer5g2b6ecvdd3xgm
A General Strategy for Hidden Markov Chain Parameterisation in Composite Feature-Spaces
[chapter]
2004
Lecture Notes in Computer Science
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A general technique for the construction of hidden Markov models (HMMs) from multiple-variable time-series observations in noisy experimental environments is set out. ...
In retaining correlation information between features, the method is potentially far more general than Gaussian mixture model HMM parameterisation methods such as Baum-Welch re-estimation, to which we ...
Most importantly, this mapping will be of an essentially stochastic nature, as required by the relation of hidden and observed states in hidden Markov modelling [cf eg. 2]. ...
doi:10.1007/978-3-540-27868-9_118
fatcat:k47glovk2ndx5pj5agisdil45m
Bayesian non-parametric hidden Markov models with applications in genomics
2010
Journal of The Royal Statistical Society Series B-statistical Methodology
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Analysing various real data sets we find significantly more accurate inference compared with state of the art hidden Markov models which use finite mixture emission distributions. ...
We propose a flexible non-parametric specification of the emission distribution in hidden Markov models and we introduce a novel methodology for carrying out the computations. ...
emission distribution as a finite mixture model. ...
doi:10.1111/j.1467-9868.2010.00756.x
pmid:21687778
pmcid:PMC3116623
fatcat:5xdtn4m2ifdbpn35wt3ey7dhlm
Inference in finite state space non parametric Hidden Markov Models and applications
2015
Statistics and computing
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Hidden Markov models (HMMs) are intensively used in various fields to model and classify data observed along a line (e.g. time). ...
In this paper, we prove that finite state space non parametric HMMs are identifiable as soon as the transition matrix of the latent Markov chain has full rank and the emission probability distributions ...
The emission distribution μ j is defined as μ j = ψ j φ . (4) A simple mixture model (i.e. when the hidden variable X i are iid) with such emission distribution is not identifiable (see Baudry et al. ...
doi:10.1007/s11222-014-9523-8
fatcat:2i4vujmg4rfajoukiep4odedhi
Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R
2019
Journal of Statistical Software
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Extending to mixture hidden Markov models (MHMMs) allows clustering data into homogeneous subsets, with or without external covariates. ...
Also other restricted variants of the MHMM can be fitted, e.g., latent class models, Markov models, mixture Markov models, or even ordinary multinomial regression models with suitable parameterization ...
Acknowledgments We also wish to thank Mervi Eerola and Jukka Nyblom as well as the editor and two anonymous referees for their helpful comments and suggestions. ...
doi:10.18637/jss.v088.i03
fatcat:nsttmgyslncq3cfquh3xxslv3u
A New Generative Feature Set Based on Entropy Distance for Discriminative Classification
[chapter]
2009
Lecture Notes in Computer Science
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The proposed score space is presented for hidden Markov models and mixture of gaussian and is experimentally validated on standard benchmark datasets; moreover it can be applied to any generative model ...
In this way, both uncertainty in the generative model learning step and "local" compliance of data observations with respect to the generative process can be represented. ...
On the right the graphical model that represent the mixture of Gaussians which can be viewed as a single slice of an hidden Markov model with gaussian emission.
1. ...
doi:10.1007/978-3-642-04146-4_23
fatcat:a24h332tarhufmfdcoqtkjq4xq
Analyzing Single-Molecule Time Series via Nonparametric Bayesian Inference
2015
Biophysical Journal
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We demonstrate these methods with applications to several diverse settings in single-molecule biophysics. ...
The interpretation of single-molecule time series has often been rooted in statistical mechanics and the theory of Markov processes. ...
In this infinite aggregated Markov model (iAMM) (35), we imagine that the hidden states appear as aggregated into one of A distinct emission distributions such that A < K. ...
doi:10.1016/j.bpj.2014.12.016
pmid:25650922
pmcid:PMC4317543
fatcat:jzqb2qglurfehfw34qbyo5g2qm
A Hybrid Hidden Markov Model for Pipeline Leakage Detection
2021
Applied Sciences
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Two comparative studies based on different numbers of states using Gaussian mixture model-hidden Markov model (GMM-HMM) and DNN-HMM are performed. ...
A long pipeline is divided into several sections and the leakage occurs in different section that is defined as different state of hidden Markov model (HMM). ...
Hidden Markov Model The hidden Markov model is a probabilistic graphical model in which the unobservable ("hidden") system state sequence is modeled by Markov chain, and hidden states can be indirectly ...
doi:10.3390/app11073138
fatcat:7bzkrqfqunfvtdyihg4aw3gxna
Abnormal Behavior Recognition Using Self-Adaptive Hidden Markov Models
[chapter]
2009
Lecture Notes in Computer Science
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A self-adaptive Hidden Markov Model (SA-HMM) based framework is proposed for behavior recognition in this paper. ...
During the training stage, the state transition and output probabilities of HMMs can be optimized through the Gaussian Mixture Models (GMMs) so the generated symbols can match the observed image features ...
Hidden Markov Models A Hidden Markov Model (HMM) [11] is a statistical model in which the system being modeled is assumed to be a Markov process with unknown parameters, and the challenge is to determine ...
doi:10.1007/978-3-642-02611-9_34
fatcat:mlhlbnmqwzh65icyyqg5sqqz7u
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