Bibliografie tematiche / Hidden state Markov model

Letteratura scientifica selezionata sul tema "Hidden state Markov model"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 26 gennaio 2023

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Articoli di riviste sul tema "Hidden state Markov model"

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Tumilaar, Kezia, Yohanes Langi e Altien Rindengan. "Hidden Markov Model". d'CARTESIAN 4, n. 1 (10 febbraio 2015): 86. http://dx.doi.org/10.35799/dc.4.1.2015.8104.

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Hidden Markov Models (HMM) is a stochastic model and is essentially an extension of Markov Chain. In Hidden Markov Model (HMM) there are two types states: the observable states and the hidden states. The purpose of this research are to understand how hidden Markov model (HMM) and to understand how the solution of three basic problems on Hidden Markov Model (HMM) which consist of evaluation problem, decoding problem and learning problem. The result of the research is hidden Markov model can be defined as . The evaluation problem or to compute probability of the observation sequence given the model P(O|) can solved by Forward-Backward algorithm, the decoding problem or to choose hidden state sequence which is optimal can solved by Viterbi algorithm and learning problem or to estimate hidden Markov model parameter to maximize P(O|) can solved by Baum – Welch algorithm. From description above Hidden Markov Model with state 3 can describe behavior from the case studies. Key words: Decoding Problem, Evaluation Problem, Hidden Markov Model, Learning Problem
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Ye, Fei, e Yifei Wang. "A Novel Method for Decoding Any High-Order Hidden Markov Model". Discrete Dynamics in Nature and Society 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/231704.

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This paper proposes a novel method for decoding any high-order hidden Markov model. First, the high-order hidden Markov model is transformed into an equivalent first-order hidden Markov model by Hadar’s transformation. Next, the optimal state sequence of the equivalent first-order hidden Markov model is recognized by the existing Viterbi algorithm of the first-order hidden Markov model. Finally, the optimal state sequence of the high-order hidden Markov model is inferred from the optimal state sequence of the equivalent first-order hidden Markov model. This method provides a unified algorithm framework for decoding hidden Markov models including the first-order hidden Markov model and any high-order hidden Markov model.
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Suharleni, Farida, Agus Widodo e Endang Wahyu H. "Hidden Markov Model Application to Transfer The Trader Online Forex Brokers". CAUCHY 2, n. 2 (4 maggio 2012): 66. http://dx.doi.org/10.18860/ca.v2i2.2222.

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<p>Hidden Markov Model is elaboration of Markov chain, which is applicable to cases that can’t directly observe. In this research, Hidden Markov Model is used to know trader’s transition to broker forex online. In Hidden Markov Model, observed state is observable part and hidden state is hidden part. Hidden Markov Model allows modeling system that contains interrelated observed state and hidden state. As observed state in trader’s transition to broker forex online is category 1, category 2, category 3, category 4, category 5 by condition of every broker forex online, whereas as hidden state is broker forex online Marketiva, Masterforex, Instaforex, FBS and Others. First step on application of Hidden Markov Model in this research is making construction model by making a probability of transition matrix (A) from every broker forex online. Next step is making a probability of observation matrix (B) by making conditional probability of five categories, that is category 1, category 2, category 3, category 4, category 5 by condition of every broker forex online and also need to determine an initial state probability (π) from every broker forex online. The last step is using Viterbi algorithm to find hidden state sequences that is broker forex online sequences which is the most possible based on model and observed state that is the five categories. Application of Hidden Markov Model is done by making program with Viterbi algorithm using Delphi 7.0 software with observed state based on simulation data. Example: By the number of observation T = 5 and observed state sequences O = (2,4,3,5,1) is found hidden state sequences which the most possible with observed state O as following : where X1 = FBS, X2 = Masterforex, X3 = Marketiva, X4 = Others, and X5 = Instaforex.</p>
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Valera, Isabel, Francisco J. R. Ruiz e Fernando Perez-Cruz. "Infinite Factorial Unbounded-State Hidden Markov Model". IEEE Transactions on Pattern Analysis and Machine Intelligence 38, n. 9 (1 settembre 2016): 1816–28. http://dx.doi.org/10.1109/tpami.2015.2498931.

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Yanyi, Xu, e Lv Jinhua. "State Recognition Based on Hidden Markov Model". International Journal of Multimedia and Ubiquitous Engineering 11, n. 2 (28 febbraio 2016): 389–98. http://dx.doi.org/10.14257/ijmue.2016.11.2.38.

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Wang, Bing, Ping Yan, Qiang Zhou e Libing Feng. "State recognition method for machining process of a large spot welder based on improved genetic algorithm and hidden Markov model". Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, n. 11 (27 gennaio 2016): 2135–46. http://dx.doi.org/10.1177/0954406215626942.

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Large spot welder is an important equipment in rail transit equipment manufacturing industry, but having the problem of low utilization rate and low effectlvely machining rate. State monitoring can master its operating states real time and comprehensively, and providing data support for state recognition. Hidden Markov model is a state classification method, but it is sensitive to the initial model parameters and easy to trap into a local optima. Genetic algorithm is a global searching method; however, it is quite poor at hill climbing and also has the problem of premature convergence. In this paper, proposing the improved genetic algorithm, and combining improved genetic algorithm and hidden Markov model, a new method of state recognition method named improved genetic algorithm–hidden Markov model is proposed. In the proposed method, improved genetic algorithm is used for optimizing the initial parameters, and hidden Markov model as a classifier to recognize the operating states for machining process. This method is also compared with the other two recognition methods named adaptive genetic algorithm–hidden Markov model and hidden Markov model, in which adaptive genetic algorithm is similarly used for optimizing the initial parameters, however hidden Markov model (in both methods) as a classifier. Experimental results show that the proposed method is very effective, and the improved genetic algorithm–hidden Markov model recognition method is superior to the adaptive genetic algorithm–hidden Markov model and hidden Markov model recognition method.
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Kimball, Steven F., e Joanne Como. "Cascaded hidden Markov model for meta-state estimation". Journal of the Acoustical Society of America 119, n. 4 (2006): 1919. http://dx.doi.org/10.1121/1.2195851.

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Goodall, Victoria L., Sam M. Ferreira, Paul J. Funston e Nkabeng Maruping-Mzileni. "Uncovering hidden states in African lion movement data using hidden Markov models". Wildlife Research 46, n. 4 (2019): 296. http://dx.doi.org/10.1071/wr18004.

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Context Direct observations of animals are the most reliable way to define their behavioural characteristics; however, to obtain these observations is costly and often logistically challenging. GPS tracking allows finer-scale interpretation of animal responses by measuring movement patterns; however, the true behaviour of the animal during the period of observation is seldom known. Aims The aim of our research was to draw behavioural inferences for a lioness with a hidden Markov model and to validate the predicted latent-state sequence with field observations of the lion pride. Methods We used hidden Markov models to model the movement of a lioness in the Kruger National Park, South Africa. A three-state log-normal model was selected as the most suitable model. The model outputs are related to collected data by using an observational model, such as, for example, a distribution for the average movement rate and/or direction of movement that depends on the underlying model states that are taken to represent behavioural states of the animal. These inferred behavioural states are validated against direct observation of the pride’s behaviour. Key results Average movement rate provided a useful alternative for the application of hidden Markov models to irregularly spaced GPS locations. The movement model predicted resting as the dominant activity throughout the day, with a peak in the afternoon. The local-movement state occurred consistently throughout the day, with a decreased proportion during the afternoon, when more resting takes place, and an increase towards the early evening. The relocating state had three peaks, namely, during mid-morning, early evening and about midnight. Because of the differences in timing of the direct observations and the GPS locations, we had to compare point observations of the true behaviour with an interval prediction of the modelled behavioural state. In 75% of the cases, the model-predicted behaviour and the field-observed behaviour overlapped. Conclusions Our data suggest that the hidden Markov modelling approach is successful at predicting a realistic behaviour of lions on the basis of the GPS location coordinates and the average movement rate between locations. The present study provided a unique opportunity to uncover the hidden states and compare the true behaviour with the inferred behaviour from the predicted state sequence. Implications Our results illustrated the potential of using hidden Markov models with movement rate as an input to understand carnivore behavioural patterns that could inform conservation management practices.
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Liao, Yiwei, Guosheng Zhao, Jian Wang e Shu Li. "Network Security Situation Assessment Model Based on Extended Hidden Markov". Mathematical Problems in Engineering 2020 (24 agosto 2020): 1–13. http://dx.doi.org/10.1155/2020/1428056.

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A network security situation assessment system based on the extended hidden Markov model is designed in this paper. Firstly, the standard hidden Markov model is expanded from five-tuple to seven-tuple, and two parameters of network defense efficiency and risk loss vector are added so that the model can describe network security situation more completely. Then, an initial algorithm of state transition matrix was defined, observation vectors were extracted from the fusion of various system security detection data, the network state transition matrix was created and modified by the observation vectors, and a solution procedure of the hidden state probability distribution sequence based on extended hidden Markov model was derived. Finally, a method of calculating risk loss vector according to the international definition was designed and the current network risk value was calculated by the hidden state probability distribution; then the global security situation was assessed. The experiment showed that the model satisfied practical applications and the assessment result is accurate and effective.
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Wang, Ning, Shu-dong Sun, Zhi-qiang Cai, Shuai Zhang e Can Saygin. "A Hidden Semi-Markov Model with Duration-Dependent State Transition Probabilities for Prognostics". Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/632702.

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Realistic prognostic tools are essential for effective condition-based maintenance systems. In this paper, a Duration-Dependent Hidden Semi-Markov Model (DD-HSMM) is proposed, which overcomes the shortcomings of traditional Hidden Markov Models (HMM), including the Hidden Semi-Markov Model (HSMM): (1) it allows explicit modeling of state transition probabilities between the states; (2) it relaxes observations’ independence assumption by accommodating a connection between consecutive observations; and (3) it does not follow the unrealistic Markov chain’s memoryless assumption and therefore it provides a more powerful modeling and analysis capability for real world problems. To facilitate the computation of the proposed DD-HSMM methodology, new forward-backward algorithm is developed. The demonstration and evaluation of the proposed methodology is carried out through a case study. The experimental results show that the DD-HSMM methodology is effective for equipment health monitoring and management.
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Tesi sul tema "Hidden state Markov model"

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Beattie, Valerie L. "Hidden Markov Model state-based noise compensation". Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259519.

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Baribault, Carl. "Meta State Generalized Hidden Markov Model for Eukaryotic Gene Structure Identification". ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/1098.

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Using a generalized-clique hidden Markov model (HMM) as the starting point for a eukaryotic gene finder, the objective here is to strengthen the signal information at the transitions between coding and non-coding (c/nc) regions. This is done by enlarging the primitive hidden states associated with individual base labeling (as exon, intron, or junk) to substrings of primitive hidden states or footprint states. Moreover, the allowed footprint transitions are restricted to those that include either one c/nc transition or none at all. (This effectively imposes a minimum length on exons and the other regions.) These footprint states allow the c/nc transitions to be seen sooner and have their contributions to the gene-structure identification weighted more heavily – yet contributing as such with a natural weighting determined by the HMM model itself according to the training data – rather than via introducing an artificial gain-parameter tuning on major transitions. The selection of the generalized HMM model is interpolated to highest Markov order on emission probabilities, and to highest Markov order (subsequence length) on the footprint states. The former is accomplished via simple count cutoff rules, the latter via an identification of anomalous base statistics near the major transitions using Shannon entropy. Preliminary indications, from applications to the C. elegans genome, are that the sensitivity/specificity (SN/SP) result for both the individual state and full exon predictions are greatly enhanced using the generalized-clique HMM when compared to the standard HMM. Here the standard HMM is represented by the choice of the smallest size of footprint state in the generalized-clique HMM. Even with these improvements, we observe that both extremely long and short exon and intron segments would go undetected without an explicit model of the duration of state. The key contributions of this effort are the full derivation and experimental confirmation of a rudimentary, yet powerful and competitive gene finding method based on a higher order hidden Markov model. With suitable extensions, this method is expected to provide superior gene finding capability – not only in the context of pre-conditioned data sets as in the evaluations cited but also in the wider context of less preconditioned and/or raw genomic data.
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Florez-Larrahondo, German. "Incremental learning of discrete hidden Markov models". Diss., Mississippi State : Mississippi State University, 2005. http://library.msstate.edu/etd/show.asp?etd=etd-05312005-141645.

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Pepper, David J. "Large hidden Markov model state interpretation as applied to automatic phonetic segmentation and labeling". Diss., Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/13537.

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Santos, Leonor Marques Pompeu dos. "Hidden Markov models for credit risk". Master's thesis, Instituto Superior de Economia e Gestão, 2015. http://hdl.handle.net/10400.5/11061.

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Mestrado em Matemática Financeira
A análise do Risco de Crédito, a avaliação do risco de defafult ou de redução do valor de mercado causado por alterações na qualidade de crédito, tem sido um tema vastamente estudado ao longo dos últimos trinta anos e é hoje mais relevante que nunca, com o mundo ainda a recuperar das consequências de uma crise financeira, na sua génese induzida por uma observação imperfeita deste tipo de risco. Tal como alguns dos modelos apresentados anteriormente, o modelo apresentado nesta dissertação assume que os eventos de default estão directamente ligados a uma variável associada ao risco, partindo de um modelo simples que assume que o default segue um Modelo Oculto de Markov Binomial de dois estados, ou seja, um modelo que considera apenas dois "estados de risco" possíveis para explicar na totalidade a ocorrência de default, e aproximando-o a um Modelo Oculto de Markov Poisson, com todas as simplificações computacionais associadas a esta aproximação, tentando, ao mesmo tempo, traduzir o modelo para um cenário menos extremo, com a inclusão de um nível de risco intermédio.
Credit Risk measurement, the evaluation of the risk of default or reduction in market value caused by changes in credit quality, has been a broadly studied subject over the last thirty years and is now more relevant than ever, when the world is still suffering the consequences of the break of a financial crisis in its genesis induced by a false observation of this kind of risk. Just like some of the previous studies, the model presented in this dissertation assumes that default events are directly connected to risk state variables, starting from a very simple model that assumes defaults to follow a two-state Binomial Hidden Markov Model, considering only two different risk categories to fully explain default occurrence, and approximating it to a Poisson Hidden Markov Model, with all the computational simplifications brought by this approximation, trying, at the same time, to translate the model into a less extreme framework, with the addition of an intermediate risk level, a "normal" risk state.
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Xie, Li Information Technology &amp Electrical Engineering Australian Defence Force Academy UNSW. "Finite horizon robust state estimation for uncertain finite-alphabet hidden Markov models". Awarded by:University of New South Wales - Australian Defence Force Academy. School of Information Technology and Electrical Engineering, 2004. http://handle.unsw.edu.au/1959.4/38664.

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In this thesis, we consider a robust state estimation problem for discrete-time, homogeneous, first-order, finite-state finite-alphabet hidden Markov models (HMMs). Based on Kolmogorov's Theorem on the existence of a process, we first present the Kolmogorov model for the HMMs under consideration. A new change of measure is introduced. The statistical properties of the Kolmogorov representation of an HMM are discussed on the canonical probability space. A special Kolmogorov measure is constructed. Meanwhile, the ergodicity of two expanded Markov chains is investigated. In order to describe the uncertainty of HMMs, we study probability distance problems based on the Kolmogorov model of HMMs. Using a change of measure technique, the relative entropy and the relative entropy rate as probability distances between HMMs, are given in terms of the HMM parameters. Also, we obtain a new expression for a probability distance considered in the existing literature such that we can use an information state method to calculate it. Furthermore, we introduce regular conditional relative entropy as an a posteriori probability distance to measure the discrepancy between HMMs when a realized observation sequence is given. A representation of the regular conditional relative entropy is derived based on the Radon-Nikodym derivative. Then a recursion for the regular conditional relative entropy is obtained using an information state method. Meanwhile, the well-known duality relationship between free energy and relative entropy is extended to the case of regular conditional relative entropy given a sub-[special character]-algebra. Finally, regular conditional relative entropy constraints are defined based on the study of the probability distance problem. Using a Lagrange multiplier technique and the duality relationship for regular conditional relative entropy, a finite horizon robust state estimator for HMMs with regular conditional relative entropy constraints is derived. A complete characterization of the solution to the robust state estimation problem is also presented.
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Wieworka, Adam. "Speech recognition using Hidden Markov Models with exponential interpolation of state parameters". Thesis, Imperial College London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286612.

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Blix, Magnus. "Essays in mathematical finance : modeling the futures price". Doctoral thesis, Handelshögskolan i Stockholm, Finansiell Ekonomi (FI), 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-534.

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This thesis consists of four papers dealing with the futures price process. In the first paper, we propose a two-factor futures volatility model designed for the US natural gas market, but applicable to any futures market where volatility decreases with maturity and varies with the seasons. A closed form analytical expression for European call options is derived within the model and used to calibrate the model to implied market volatilities. The result is used to price swaptions and calendar spread options on the futures curve. In the second paper, a financial market is specified where the underlying asset is driven by a d-dimensional Wiener process and an M dimensional Markov process. On this market, we provide necessary and, in the time homogenous case, sufficient conditions for the futures price to possess a semi-affine term structure. Next, the case when the Markov process is unobservable is considered. We show that the pricing problem in this setting can be viewed as a filtering problem, and we present explicit solutions for futures. Finally, we present explicit solutions for options on futures both in the observable and unobservable case. The third paper is an empirical study of the SABR model, one of the latest contributions to the field of stochastic volatility models. By Monte Carlo simulation we test the accuracy of the approximation the model relies on, and we investigate the stability of the parameters involved. Further, the model is calibrated to market implied volatility, and its dynamic performance is tested. In the fourth paper, co-authored with Tomas Björk and Camilla Landén, we consider HJM type models for the term structure of futures prices, where the volatility is allowed to be an arbitrary smooth functional of the present futures price curve. Using a Lie algebraic approach we investigate when the infinite dimensional futures price process can be realized by a finite dimensional Markovian state space model, and we give general necessary and sufficient conditions, in terms of the volatility structure, for the existence of a finite dimensional realization. We study a number of concrete applications including the model developed in the first paper of this thesis. In particular, we provide necessary and sufficient conditions for when the induced spot price is a Markov process. We prove that the only HJM type futures price models with spot price dependent volatility structures, generically possessing a spot price realization, are the affine ones. These models are thus the only generic spot price models from a futures price term structure point of view.
Diss. Stockholm : Handelshögskolan, 2004
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Baker, Adam. "Temporal dynamics of resting state brain connectivity as revealed by magnetoencephalography". Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:ad9a825f-7036-4597-89d3-a7dfc8bb0641.

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Explorations into the organisation of spontaneous activity within the brain have demonstrated the existence of networks of temporally correlated activity, consisting of brain areas that share similar cognitive or sensory functions. These so-called resting state networks (RSNs) emerge spontaneously during rest and disappear in response to overt stimuli or cognitive demands. In recent years, the study of RSNs has emerged as a valuable tool for probing brain function, both in the healthy brain and in disorders such as schizophrenia, Alzheimer’s disease and Parkinson’s disease. However, analyses of these networks have so far been limited, in part due to assumptions that the patterns of neuronal activity that underlie these networks remain constant over time. Moreover, the majority of RSN studies have used functional magnetic resonance imaging (fMRI), in which slow fluctuations in the level of oxygen in the blood are used as a proxy for the activity within a given brain region. In this thesis we develop the use of magnetoencephalography (MEG) to study resting state functional connectivity. Unlike fMRI, MEG provides a direct measure of neuronal activity and can provide novel insights into the temporal dynamics that underlie resting state activity. In particular, we focus on the application of non- stationary analysis methods, which are able to capture fast temporal changes in activity. We first develop a framework for preprocessing MEG data and measuring interactions within different RSNs (Chapter 3). We then extend this framework to assess temporal variability in resting state functional connectivity by applying time- varying measures of interactions and show that within-network functional connectivity is underpinned by non-stationary temporal dynamics (Chapter 4). Finally we develop a data driven approach based on a hidden Markov model for inferring short lived connectivity states from resting state and task data (Chapter 5). By applying this approach to data from multiple subjects we reveal transient states that capture short lived patterns of neuronal activity (Chapter 6).
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Ejnestrand, Ida, e Linnéa Jakobsson. "Object Tracking based on Eye Tracking Data : A comparison with a state-of-the-art video tracker". Thesis, Linköpings universitet, Datorseende, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166007.

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The process of locating moving objects through video sequences is a fundamental computer vision problem. This process is referred to as video tracking and has a broad range of applications. Even though video tracking is an open research topic that have received much attention during recent years, developing accurate and robust algorithms that can handle complicated tracking tasks and scenes is still challenging. One challenge in computer vision is to develop systems that like humans can understand, interpret and recognize visual information in different situations. In this master thesis work, a tracking algorithm based on eye tracking data is proposed. The aim was to compare the tracking performance of the proposed algorithm with a state-of-the-art video tracker. The algorithm was tested on gaze signals from five participants recorded with an eye tracker while the participants were exposed to dynamic stimuli. The stimuli were moving objects displayed on a stationary computer screen. The proposed algorithm is working offline meaning that all data is collected before analysis. The results show that the overall performance of the proposed eye tracking algorithm is comparable to the performance of a state-of-the-art video tracker. The main weaknesses are low accuracy for the proposed eye tracking algorithm and handling of occlusion for the video tracker. We also suggest a method for using eye tracking as a complement to object tracking methods. The results show that the eye tracker can be used in some situations to improve the tracking result of the video tracker. The proposed algorithm can be used to help the video tracker to redetect objects that have been occluded or for some other reason are not detected correctly. However, ATOM brings higher accuracy.
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Libri sul tema "Hidden state Markov model"

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Walsh, John Leclerc. Extraction of single channel current from correlated noise via a hidden Markov model. [s.l: s.n.], 1992.

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Penny, D. Modeling the covarion model of molecular evolution by hidden Markov chains. Palmerston North, N.Z: Massey University College of Sciences, 1998.

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Jacobs, Patricia A. Estimation of the probability of a long time to the first entrance to a state in a semi-Markov model. Monterey, Calif: Naval Postgraduate School, 1987.

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Nguyen, Van O. Analysis of the U.S. Marine Corps' steady state Markov model for forecasting annual first-term enlisted classification requirements. Monterey, Calif: Naval Postgraduate School, 1997.

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Howes, Andrew, Xiuli Chen, Aditya Acharya e Richard L. Lewis. Interaction as an Emergent Property of a Partially Observable Markov Decision Process. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0011.

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In this chapter we explore the potential advantages of modeling the interaction between a human and a computer as a consequence of a Partially Observable Markov Decision Process (POMDP) that models human cognition. POMDPs can be used to model human perceptual mechanisms, such as human vision, as partial (uncertain) observers of a hidden state are possible. In general, POMDPs permit a rigorous definition of interaction as the outcome of a reward maximizing stochastic sequential decision processes. They have been shown to explain interaction between a human and an environment in a range of scenarios, including visual search, interactive search and sense-making. The chapter uses these scenarios to illustrate the explanatory power of POMDPs in HCI. It also shows that POMDPs embrace the embodied, ecological and adaptive nature of human interaction.
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Sun, Shuying. Haplotype inference using a hidden Markov model with efficient Markov chain sampling. 2007, 2007.

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Miao, Qiang. Application of wavelets and hidden Markov model in condition-based maintenance. 2005.

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A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding. Providence, USA: Brown University, 2019.

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Analysis of the U.S. Marine Corps' Steady State Markov Model for Forecasting Annual First-Term Enlisted Classification Requirements. Storming Media, 1997.

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Blee, Lisa, e Jean M. O'Brien. Monumental Mobility. University of North Carolina Press, 2019. http://dx.doi.org/10.5149/northcarolina/9781469648408.001.0001.

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Installed at Plymouth, Massachusetts, in 1921 to commemorate the tercentenary of the landing of the Pilgrims, Cyrus Dallin's statue Massasoit was intended to memorialize the Pokanoket Massasoit (leader) as a welcoming diplomat and participant in the mythical first Thanksgiving. But after the statue's unveiling, Massasoit began to move and proliferate in ways one would not expect of generally stationary monuments tethered to place. The plaster model was donated to the artist's home state of Utah and prominently displayed in the state capitol; half a century later, it was caught up in a surprising case of fraud in the fine arts market. Versions of the statue now stand on Brigham Young University's campus; at an urban intersection in Kansas City, Missouri; and in countless homes around the world in the form of souvenir statuettes. The surprising story of this monumental statue reveals much about the process of creating, commodifying, and reinforcing the historical memory of Indigenous people. Dallin's statue, set alongside the historical memory of the actual Massasoit and his mythic collaboration with the Pilgrims, shows otherwise hidden dimensions of American memorial culture: an elasticity of historical imagination, a tight-knit relationship between consumption and commemoration, and the twin impulses to sanitize and grapple with the meaning of settler-colonialism.
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Capitoli di libri sul tema "Hidden state Markov model"

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Fahrmeir, Ludwig, e Gerhard Tutz. "State Space and Hidden Markov Models". In Multivariate Statistical Modelling Based on Generalized Linear Models, 331–83. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3454-6_8.

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Zucchini, Walter, Iain L. MacDonald e Roland Langrock. "Continuous-valued state processes". In Hidden Markov Models for Time Series, 155–63. Second edition / Walter Zucchini, Iain L. MacDonald, and Roland Langrock. | Boca Raton : Taylor & Francis, 2016. | Series: Monographs on statistics and applied probability ; 150 | “A CRC title.”: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/b20790-11.

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Zucchini, Walter, Iain L. MacDonald e Roland Langrock. "Forecasting, decoding and state prediction". In Hidden Markov Models for Time Series, 81–95. Second edition / Walter Zucchini, Iain L. MacDonald, and Roland Langrock. | Boca Raton : Taylor & Francis, 2016. | Series: Monographs on statistics and applied probability ; 150 | “A CRC title.”: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/b20790-5.

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Zucchini, Walter, Iain L. MacDonald e Roland Langrock. "HMMs with general state-dependent distribution". In Hidden Markov Models for Time Series, 133–44. Second edition / Walter Zucchini, Iain L. MacDonald, and Roland Langrock. | Boca Raton : Taylor & Francis, 2016. | Series: Monographs on statistics and applied probability ; 150 | “A CRC title.”: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/b20790-9.

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Chopin, Nicolas, e Omiros Papaspiliopoulos. "Finite State-Spaces and Hidden Markov Models". In Springer Series in Statistics, 67–71. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47845-2_6.

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Gavaldà, Ricard, Philipp W. Keller, Joelle Pineau e Doina Precup. "PAC-Learning of Markov Models with Hidden State". In Lecture Notes in Computer Science, 150–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11871842_18.

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Luktarhan, Nurbol, Xue Jia, Liang Hu e Nannan Xie. "Multi-stage Attack Detection Algorithm Based on Hidden Markov Model". In Web Information Systems and Mining, 275–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33469-6_37.

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Tang, Yuan Y., Yuhua Hou, Jinping Song e Xiaoyi Yang. "Mixture-State Document Segmentation Using Wavelet-Domain Hidden Markov Tree Models". In Wavelet Analysis and Its Applications, 230–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45333-4_29.

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Wu, Chung-Hsien, Jen-Chun Lin e Wen-Li Wei. "Audiovisual Emotion Recognition Using Semi-Coupled Hidden Markov Model with State-Based Alignment Strategy". In Emotion Recognition, 493–513. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781118910566.ch19.

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Lin, Jen-Chun, Chung-Hsien Wu e Wen-Li Wei. "Semi-Coupled Hidden Markov Model with State-Based Alignment Strategy for Audio-Visual Emotion Recognition". In Affective Computing and Intelligent Interaction, 185–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24600-5_22.

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Atti di convegni sul tema "Hidden state Markov model"

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Kimber, D., e M. Bush. "Situated state hidden Markov models". In Proceedings of ICASSP '93. IEEE, 1993. http://dx.doi.org/10.1109/icassp.1993.319350.

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Beattie, V. L., e Steve J. Young. "Hidden Markov model state-based cepstral noise compensation". In 2nd International Conference on Spoken Language Processing (ICSLP 1992). ISCA: ISCA, 1992. http://dx.doi.org/10.21437/icslp.1992-171.

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Moon, Todd K., e Jacob H. Gunther. "Quasicontinuous state hidden Markov models incorporating state histories". In 2014 48th Asilomar Conference on Signals, Systems and Computers. IEEE, 2014. http://dx.doi.org/10.1109/acssc.2014.7094843.

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Borisov, A., e G. Miller. "Hidden Markov model approach to TCP link state tracking". In 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601). IEEE, 2004. http://dx.doi.org/10.1109/cdc.2004.1429318.

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Xia, Tong, Yue Yu, Fengli Xu, Funing Sun, Diansheng Guo, Depeng Jin e Yong Li. "Understanding Urban Dynamics via State-sharing Hidden Markov Model". In The World Wide Web Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3308558.3313453.

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Ning Li e De Xu. "Action recognition using weighted three-state Hidden Markov Model". In 2008 9th International Conference on Signal Processing (ICSP 2008). IEEE, 2008. http://dx.doi.org/10.1109/icosp.2008.4697400.

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Rasmussen, Peter F., e Bertug Akintug. "Drought Frequency Analysis with a Hidden State Markov Model". In World Water and Environmental Resources Congress 2004. Reston, VA: American Society of Civil Engineers, 2004. http://dx.doi.org/10.1061/40737(2004)261.

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Wolfertstetter, F., e Günther Ruske. "Discriminative state-weighting in hidden Markov models". In 3rd International Conference on Spoken Language Processing (ICSLP 1994). ISCA: ISCA, 1994. http://dx.doi.org/10.21437/icslp.1994-58.

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Kempe, André. "Finite state transducers approximating Hidden Markov Models". In the eighth conference. Morristown, NJ, USA: Association for Computational Linguistics, 1997. http://dx.doi.org/10.3115/979617.979676.

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Kempe, André. "Finite state transducers approximating Hidden Markov Models". In the 35th annual meeting. Morristown, NJ, USA: Association for Computational Linguistics, 1997. http://dx.doi.org/10.3115/976909.979676.

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Rapporti di organizzazioni sul tema "Hidden state Markov model"

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Ainsleigh, Phillip L. Theory of Continuous-State Hidden Markov Models and Hidden Gauss-Markov Models. Fort Belvoir, VA: Defense Technical Information Center, marzo 2001. http://dx.doi.org/10.21236/ada415930.

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Yang, Jie, e Yangsheng Xu. Hidden Markov Model for Gesture Recognition. Fort Belvoir, VA: Defense Technical Information Center, maggio 1994. http://dx.doi.org/10.21236/ada282845.

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Chan, A. D., K. Englehart, B. Hudgins e D. F. Lovely. Hidden Markov Model Classification of Myoelectric Signals in Speech. Fort Belvoir, VA: Defense Technical Information Center, ottobre 2001. http://dx.doi.org/10.21236/ada410037.

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Baggenstoss, Paul M. A Multi-Resolution Hidden Markov Model Using Class-Specific Features. Fort Belvoir, VA: Defense Technical Information Center, gennaio 2008. http://dx.doi.org/10.21236/ada494596.

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Boyle, James P., Douglas J. Hentschel e Theodore J. Thompson. Markov Chains for Random Urinalysis II: Age-Test Model with Absorbing State. Fort Belvoir, VA: Defense Technical Information Center, maggio 1993. http://dx.doi.org/10.21236/ada265557.

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Соловйов, Володимир Миколайович, V. Saptsin e D. Chabanenko. Financial time series prediction with the technology of complex Markov chains. Transport and Telecommunication Institute, 2010. http://dx.doi.org/10.31812/0564/1145.

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Abstract (sommario):
In this research the technology of complex Markov chains, i.e. Markov chains with a memory is applied to forecast financial time-series. The main distinction of complex or high-order Markov chains [1] and simple first-order ones is the existing of after effect or memory. The high-order Markov chains can be simplified to first-order ones by generalizing the states in Markov chains. Considering the “generalized state” as the sequence of states makes a possibility to model high-order Markov chains like first-order ones. The adaptive method of defining the states is proposed, it is concerned with the statistic properties of price returns [2]. According to the fundamental principles of quantum measurement theories, the measurement procedure impacts not only on the result of the measurement, but also on the state of the measured system, and the behaviour of this system in the future remains undefined, despite of the precision of the measurement. This statement, in our opinion, is general and is true not only for physical systems, but to any complex systems [3].
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Soloviev, V., V. Saptsin e D. Chabanenko. Financial time series prediction with the technology of complex Markov chains. Брама-Україна, 2014. http://dx.doi.org/10.31812/0564/1305.

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Abstract (sommario):
In this research the technology of complex Markov chains, i.e. Markov chains with a memory is applied to forecast financial time-series. The main distinction of complex or high-order Markov Chains and simple first-ord yer ones is the existing of aftereffect or memory. The high-order Markov chains can be simplified to first-order ones by generalizing the states in Markov chains. Considering the «generalized state» as the sequence of states makes a possibility to model high-order Markov chains like first-order ones. The adaptive method of defining the states is proposed, it is concerned with the statistic properties of price returns.
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Соловйов, Володимир Миколайович, Vladimir Saptsin e Dmitry Chabanenko. Prediction of financial time series with the technology of high-order Markov chains. AGSOE, marzo 2009. http://dx.doi.org/10.31812/0564/1131.

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Abstract (sommario):
In this research the technology of complex Markov chains, i.e. Markov chains with a memory is applied to forecast the financial time-series. The high-order Markov chains can be simplified to first-order ones by generalizing the states in Markov chains. Considering the *generalized state* as the sequence of states makes a possibility to model high-order Markov chains like first-order ones. The adaptive method of defining the states is proposed, it is concerned with the statistic properties of price returns. The algorithm of prediction includes the next steps: (1) Generate the hierarchical set of time discretizations; (2) Reducing the discretiza- tion of initial data and doing prediction at the every time-level (3) Recurrent conjunction of prediction series of different discretizations in a single time-series. The hierarchy of time discretizations gives a possibility to review long-memory properties of the series without increasing the order of the Markov chains, to make prediction on the different frequencies of the series. The technology is tested on several time-series, including: EUR/USD Forex course, the World’s indices, including Dow Jones, S&P 500, RTS, PFTS and other.
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Méndez-Vizcaíno, Juan C., e Nicolás Moreno-Arias. A Global Shock with Idiosyncratic Pains: State-Dependent Debt Limits for LATAM during the COVID-19 pandemic. Banco de la República, ottobre 2021. http://dx.doi.org/10.32468/be.1175.

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Abstract (sommario):
Fiscal sustainability in five of the largest Latin American economies is examined before and after the COVID-19 pandemic. For this purpose, the DSGE model in Bi(2012) and Hürtgen (2020) is used to estimate the Fiscal Limits and Fiscal Spaces for Peru, Chile, Mexico, Colombia, and Brazil. These estimates advance the empirical literature for Latin America on fiscal sustainability by offering new calculations stemming from a structural framework with alluring novel features: government default on the intensive margin; dynamic Laffer curves; utility-based stochastic discount factor; and a Markov-Switching process for public transfers with an explosive regime. The most notable additions to the existing literature for Latin America are the estimations of entire distributions of public debt limits for various default probabilities and that said limits critically hinge on both current and future states. Results obtained indicate notorious contractions of Fiscal Spaces among all countries during the pandemic, but the sizes of these were very heterogeneous. Countries that in 2019 had positive spaces and got closer to negative spaces in 2020, have since seen deterioration of their sovereign debt ratings or outlooks. Colombia was the only country to lose its positive Fiscal Space and investment grade, thereby joining Brazil, the previously sole member of both groups
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Shomer, Ilan, Ruth E. Stark, Victor Gaba e James D. Batteas. Understanding the hardening syndrome of potato (Solanum tuberosum L.) tuber tissue to eliminate textural defects in fresh and fresh-peeled/cut products. United States Department of Agriculture, novembre 2002. http://dx.doi.org/10.32747/2002.7587238.bard.

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The project sought to understand factors and mechanisms involved in the hardening of potato tubers. This syndrome inhibits heat softening due to intercellular adhesion (ICA) strengthening, compromising the marketing of industrially processed potatoes, particularly fresh peeled-cut or frozen tubers. However, ICA strengthening occurs under conditions which are inconsistent with the current ideas that relate it to Ca-pectate following pectin methyl esterase (PME) activity or to formation of rhamnogalacturonan (RG)-II-borate. First, it was necessary to induce strengthening of the middle lamellar complex (MLX) and the ICA as a stress response in some plant parenchyma. As normally this syndrome does not occur uniformly enough to study it, we devised an efficient model in which ICA-strengthening is induced consistently under simulated stress by short-chain, linear, mono-carboxylic acid molecules (OAM), at 65 oC [appendix 1 (Shomer&Kaaber, 2006)]. This rapid strengthening was insufficient for allowing the involved agents assembly to be identifiable; but it enabled us to develop an efficient in vitro system on potato tuber parenchyma slices at 25 ºC for 7 days, whereas unified stress was reliably simulated by OAMs in all the tissue cells. Such consistent ICA-strengthening in vitro was found to be induced according to the unique physicochemical features of each OAM as related to its lipophilicity (Ko/w), pKa, protonated proportion, and carbon chain length by the following parameters: OAM dissociation constant (Kdiss), adsorption affinity constant (KA), number of adsorbed OAMs required for ICA response (cooperativity factor) and the water-induced ICA (ICAwater). Notably, ICA-strengthening is accompanied by cell sap leakage, reflecting cell membrane rupture. In vitro, stress simulation by OAMs at pH<pKa facilitated the consistent assembly of ICAstrengthening agents, which we were able to characterize for the first time at the molecular level within purified insoluble cell wall of ICA-strengthened tissue. (a) With solid-state NMR, we established the chemical structure and covalent binding to cell walls of suberin-like agents associated exclusively with ICA strengthening [appendix 3 (Yu et al., 2006)]; (b) Using proteomics, 8 isoforms of cell wall-bound patatin (a soluble vacuolar 42-kDa protein) were identified exclusively in ICA-strengthened tissue; (c) With light/electron microscopy, ultrastructural characterization, histochemistry and immunolabeling, we co-localized patatin and pectin in the primary cell wall and prominently in the MLX; (d) determination of cell wall composition (pectin, neutral sugars, Ca-pectate) yielded similar results in both controls and ICA-strengthened tissue, implicating factors other than PME activity, Ca2+ or borate ions; (e) X-ray powder diffraction experiments revealed that the cellulose crystallinity in the cell wall is masked by pectin and neutral sugars (mainly galactan), whereas heat or enzymatic pectin degradation exposed the crystalline cellulose structure. Thus, we found that exclusively in ICA-strengthened tissue, heat-resistant pectin is evident in the presence of patatin and suberinlike agents, where the cellulose crystallinity was more hidden than in fresh control tissue. Conclusions: Stress response ICA-strengthening is simulated consistently by OAMs at pH< pKa, although PME and formation of Ca-pectate and RG-II-borate are inhibited. By contrast, at pH>pKa and particularly at pH 7, ICA-strengthening is mostly inhibited, although PME activity and formation of Ca-pectate or RG-II-borate are known to be facilitated. We found that upon stress, vacuolar patatin is released with cell sap leakage, allowing the patatin to associate with the pectin in both the primary cell wall and the MLX. The stress response also includes formation of covalently bound suberin-like polyesters within the insoluble cell wall. The experiments validated the hypotheses, thus led to a novel picture of the structural and molecular alterations responsible for the textural behavior of potato tuber. These findings represent a breakthrough towards understanding of the hardening syndrome, laying the groundwork for potato-handling strategies that assure textural quality of industrially processed particularly in fresh peeled cut tubers, ready-to-prepare and frozen preserved products.
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