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Properties of the Statistical Complexity Functional and Partially Deterministic HMMs

Wolfgang Löhr
2009 Entropy  
We also prove that, given the past output of a partially deterministic hidden Markov model (HMM), the uncertainty of the internal state is constant over time and knowledge of the internal state gives no  ...  We investigate its more abstract properties as a non-linear functional on the space of processes and show its close relation to Knight's prediction process.  ...  Acknowledgements I am thankful to Nihat Ay for introducing me to computational mechanics, discussions, and all kinds of scientific support.  ... 
doi:10.3390/e110300385 fatcat:n6hc5jxcofderoqgy2nqxw2dsu

Using statistical traffic analysis to calculate the confidential means of information transmission

Нух Таха Насіф
2021 Наука і техніка Повітряних Сил Збройних Сил України  
The article considers the modeling of security problems in the Internet as stochastic systems. This allows you to find flaws in existing security systems and offer new solutions.  ...  Formulation of planning problems as a decentralized partially observable Markov decision-making process (DEC-POMDP) allows to make decisions in a distributed manner on each device without the need of centralized  ...  Conclusions In this work the problem, arising at constructing or study of HMM class of observation data is analyzed and solved, which allows precisely to define both data, used for model training and the  ... 
doi:10.30748/nitps.2021.42.15 doaj:d02ff2d113de4de6be28798b059bd59e fatcat:qk4xjle6ybhudge2ihl5rz24k4

Assessing the resilience of stochastic dynamic systems under partial observability

Jacopo Panerati, Nicolas Schwind, Stefan Zeltner, Katsumi Inoue, Giovanni Beltrame, Hedvig Kjellström
2018 PLoS ONE  
Resilience is a property of major interest for the design and analysis of generic complex systems.  ...  This allows us to more realistically model the stochastic evolution and partial observability of many complex real-world environments.  ...  Acknowledgments The authors would like to thank the reviewers for their comments, the constructive feedback, and the opportunity to improve this manuscript.  ... 
doi:10.1371/journal.pone.0202337 pmid:30138373 fatcat:tave2nw3pzggno3krhdbjqc6au

Automated Generation of Reduced Stochastic Weather Models I: Simultaneous Dimension and Model Reduction for Time Series Analysis

Illia Horenko, Rupert Klein, Stamen Dolaptchiev, Christof Schütte
2008 Multiscale Modeling & simulation  
The approach is based on the combination of hidden Markov models (HMMs) with localized principal component analysis (PCA) and fitting of multidimensional stochastic differential equations (SDE).  ...  We derive explicit estimators for PCA-SDE model parameters and employ the Expectation Maximization algorithm for numerical optimization of HMM-PCA-SDE parameters.  ...  Majda for valuable hints and suggestions concerning the topic of the manuscript. Thanks also to Vladimir Petukhov for constant encouragement and for providing meteorological background information.  ... 
doi:10.1137/060670535 fatcat:2ffhjbgpenftxk26rcqn4opuoe

Computational Models for Speech Production [chapter]

Li Deng
1999 Computational Models of Speech Pattern Processing  
As an example, a statistical task-dynamic model of speech production is described, motivated by the original deterministic version of the model and targeted for integrated-multilingual speech recognition  ...  Strengths and weaknesses of these two styles of speech models are analyzed, pointing to the need to integrate the respective strengths while eliminating the respective weaknesses.  ...  Acknowledgements Over the past several years and on the subject matter of the three tutorial papers written by the author in this book, many discussions with or experimental contributions from the following  ... 
doi:10.1007/978-3-642-60087-6_20 fatcat:ikv62f4korgtpataxm3dbhpvtq

Page 7731 of Mathematical Reviews Vol. , Issue 96m [page]

1996 Mathematical Reviews  
processes for which the coefficient functions b, are the Fourier transforms of complex measures m,, a € R.  ...  Thus the parameters of an HMM are 6),:--,0,, the transition probabilities of the Markov chain {X(k)} and r, which is referred to as the order of the HMM.  ... 

Hidden Markov Models for Automated Protocol Learning [chapter]

Sean Whalen, Matt Bishop, James P. Crutchfield
2010 Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering  
While algorithms exist to find local optima for some parameters, the number of states must always be specified and directly impacts the accuracy and generality of the model.  ...  We apply the -machine-a special type of HMM-to the task of constructing network protocol models solely from network traffic.  ...  , and Computational Sciences of the U.S.  ... 
doi:10.1007/978-3-642-16161-2_24 fatcat:2srkgipoaraqfisjnmvyp3v7ie

Deterministically annealed design of hidden Markov model speech recognizers

A.V. Rao, K. Rose
2001 IEEE Transactions on Speech and Audio Processing  
DA is derived from fundamental principles of statistical physics and information theory.  ...  Direct minimization of the error rate is difficult because of the complex nature of the cost surface, and has only been addressed recently by discriminative design methods such as generalized probabilistic  ...  REMAP is claimed to have better optimality properties than ML and can be applied to design any statistical classifier.  ... 
doi:10.1109/89.902278 fatcat:yn4y3litcrdcbfxhvxcuoxsunu

Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data [chapter]

R. R. Brooks, Lu Yu, Yu Fu, Guthrie Cordone, Jon Oakley, Xingsi Zhong
2018 Proceedings of International Symposium on Sensor Networks, Systems and Security  
For contrast, we include a related data-driven statistical inferencing approach that detects and localizes radiation sources.  ...  This chapter provides an overview of our approach with numerous practical applications.  ...  For each deterministic HMM there is an equivalent standard HMM and vice versa [37] . This deterministic property helps us infer HMMs from observations. Schwier et al.  ... 
doi:10.1007/978-3-319-75683-7_20 fatcat:v44qzknbszgezkqobikgr7iomm

Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences [article]

Cosma Shalizi, Kristina Lisa Klinkner
2014 arXiv   pre-print
We discuss the reliability of CSSR, its data requirements, and its performance in simulations.  ...  superior to the former and at least comparable to the latter.  ...  Haslinger for reading the MS., J. Lindsey and S. Iacus for R help, E. van Nimwegen for providing a preprint of [29] and sug-gesting that something similar might infer causal states, K.  ... 
arXiv:1408.2025v1 fatcat:muphzfe4vfddfiwb5sltm52xze

Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences [article]

Cosma Rohilla Shalizi, Kristina Lisa Shalizi
2004 arXiv   pre-print
We discuss the reliability of CSSR, its data requirements, and its performance in simulations.  ...  superior to the former and at least comparable to the latter.  ...  Haslinger for reading the MS., J. Lindsey and S. Iacus for R help, E. van Nimwegen for providing a preprint of [29] and sug-gesting that something similar might infer causal states, K.  ... 
arXiv:cs/0406011v1 fatcat:ip5mppjconalhisll3swwjud54

Analysis and modeling of wind directions time series

Salvatore Basile, Riccardo Burlon, Davide Gurrera
2013 2013 International Conference on Renewable Energy Research and Applications (ICRERA)  
This work aims at studying some aspects of wind directions in Italy and supplying appropriate models.  ...  A comparison is presented between independent mixture and Hidden Markov models, which seem to be appropriate as far as the series we studied.  ...  Authors acknowledge the collaboration of the Servizio Informativo Agrometeorologico Siciliano for providing the data.  ... 
doi:10.1109/icrera.2013.6749932 fatcat:mzsggkifubai3afqveoonuvnbu

A Revision of Coding Theory for Learning from Language

2004 Electronical Notes in Theoretical Computer Science  
A differentiation 1 [1] has shown that Zipf's law is met at least by strings of independently tossed letters and spaces. [19] reports on change in the law's exponent from −1 to −3 for ranks ≈ 10 4 , which  ...  Elements of a quantitative-symbolic theory of human language communication based on power-law entropic sublinearity are induced from independent results in quantitative linguistics, statistical NLP, information  ...  One of powerful methods of the theory is to map formal properties of the language L into analytic properties of its generating function G(z) = n≥0 g(n)z n , where g(n) is the number of strings of length  ... 
doi:10.1016/s1571-0661(05)82574-5 fatcat:t4r6hquy6zc43ew5v7hx5eucey

Regime prediction and predictability in nonlinear dynamical systems

F. Kwasniok
2008 The European Physical Journal Special Topics  
Prediction and predictability properties of nonlinear dynamical systems are diagnosed and analysed empirically using nonlinear time series analysis techniques.  ...  The regimes and the transition probabilities between them are determined simultaneously by fitting a hidden Markov model to a time series of the system.  ...  Reducing the step size by a factor of 10 does not change the statistical properties of the system.  ... 
doi:10.1140/epjst/e2008-00847-y fatcat:nayvj4ovczga5fotadjeao6wii

Part-of-Speech Tagging and Partial Parsing [chapter]

S. Abney
1997 Text, Speech and Language Technology  
Those available for free include an HMM tagger implemented at Xerox [23], the Brill tagger, and the Multext tagger [8]. 1 Moreover, taggers have now been developed for a number of different languages.  ...  In another line of development, hidden Markov models (HMMs) were imported from speech recognition and applied to tagging, by Bahl and Mercer [9], Derouault and Merialdo [26], and Church [20].  ...  As concerns accuracy figures-for taggers generally, not just for HMM taggersit is good to remember the maxim, "there are lies, damned lies, and statistics."  ... 
doi:10.1007/978-94-017-1183-8_4 fatcat:nkdxn66w7beita4esb5s55dx44
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