Thematische Bibliographien / Hidden statistics

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Auswahl der wissenschaftlichen Literatur zum Thema „Hidden statistics“

Autor: Grafiati

Veröffentlicht am 30. Juni 2021

Zuletzt aktualisiert am 14. Februar 2022

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Zeitschriftenartikel zum Thema "Hidden statistics"

Flajolet, Philippe, Wojciech Szpankowski und Brigitte Vallée. „Hidden word statistics“. Journal of the ACM 53, Nr. 1 (Januar 2006): 147–83. http://dx.doi.org/10.1145/1120582.1120586.

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Zak, Michail. „Hidden statistics of Schrödinger equation.“ Physics Essays 22, Nr. 2 (01.06.2009): 173–78. http://dx.doi.org/10.4006/1.3123664.

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Teague, Michael. „Statistics may ignore hidden crime“. Probation Journal 52, Nr. 1 (März 2005): 76–77. http://dx.doi.org/10.1177/0264550505050627.

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Mevel, L., und L. Finesso. „Asymptotical Statistics of Misspecified Hidden Markov Models“. IEEE Transactions on Automatic Control 49, Nr. 7 (Juli 2004): 1123–32. http://dx.doi.org/10.1109/tac.2004.831156.

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Ge, Y. F. „Hidden topological Zn symmetry and fractional statistics“. Physics Letters A 166, Nr. 3-4 (Juni 1992): 185–87. http://dx.doi.org/10.1016/0375-9601(92)90359-t.

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Farcomeni, Alessio. „Hidden Markov partition models“. Statistics & Probability Letters 81, Nr. 12 (Dezember 2011): 1766–70. http://dx.doi.org/10.1016/j.spl.2011.07.012.

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Sebastianelli, Rose, und Susan Trussler. „International Content as Hidden Curriculum in Business Statistics“. Journal of Teaching in International Business 18, Nr. 1 (November 2006): 73–87. http://dx.doi.org/10.1300/j066v18n01_05.

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BAŞÇI, Sıdıka. „Using Numbers to Persuade: Hidden Rhetoric of Statistics“. International Econometric Review 12, Nr. 1 (08.06.2020): 75–97. http://dx.doi.org/10.33818/ier.747554.

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Silveira, Fernando, und Edmundo de Souza e Silva. „Predicting packet loss statistics with hidden Markov models“. ACM SIGMETRICS Performance Evaluation Review 35, Nr. 3 (Dezember 2007): 19–21. http://dx.doi.org/10.1145/1328690.1328698.

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Morse, Peter K., und Eric I. Corwin. „Hidden symmetries in jammed systems“. Journal of Statistical Mechanics: Theory and Experiment 2016, Nr. 7 (04.07.2016): 074009. http://dx.doi.org/10.1088/1742-5468/2016/07/074009.

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Dissertationen zum Thema "Hidden statistics"

Murphy, James Kevin. „Hidden states, hidden structures : Bayesian learning in time series models“. Thesis, University of Cambridge, 2014. https://www.repository.cam.ac.uk/handle/1810/250355.

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This thesis presents methods for the inference of system state and the learning of model structure for a number of hidden-state time series models, within a Bayesian probabilistic framework. Motivating examples are taken from application areas including finance, physical object tracking and audio restoration. The work in this thesis can be broadly divided into three themes: system and parameter estimation in linear jump-diffusion systems, non-parametric model (system) estimation and batch audio restoration. For linear jump-diffusion systems, efficient state estimation methods based on the variable rate particle filter are presented for the general linear case (chapter 3) and a new method of parameter estimation based on Particle MCMC methods is introduced and tested against an alternative method using reversible-jump MCMC (chapter 4). Non-parametric model estimation is examined in two settings: the estimation of non-parametric environment models in a SLAM-style problem, and the estimation of the network structure and forms of linkage between multiple objects. In the former case, a non-parametric Gaussian process prior model is used to learn a potential field model of the environment in which a target moves. Efficient solution methods based on Rao-Blackwellized particle filters are given (chapter 5). In the latter case, a new way of learning non-linear inter-object relationships in multi-object systems is developed, allowing complicated inter-object dynamics to be learnt and causality between objects to be inferred. Again based on Gaussian process prior assumptions, the method allows the identification of a wide range of relationships between objects with minimal assumptions and admits efficient solution, albeit in batch form at present (chapter 6). Finally, the thesis presents some new results in the restoration of audio signals, in particular the removal of impulse noise (pops and clicks) from audio recordings (chapter 7).

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Van, Gael Jurgen. „Bayesian nonparametric hidden Markov models“. Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610196.

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Varenius, Malin. „Using Hidden Markov Models to Beat OMXS30“. Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-409780.

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Tillman, Måns. „On-Line Market Microstructure Prediction Using Hidden Markov Models“. Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208312.

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Over the last decades, financial markets have undergone dramatic changes. With the advent of the arbitrage pricing theory, along with new technology, markets have become more efficient. In particular, the new high-frequency markets, with algorithmic trading operating on micro-second level, make it possible to translate ”information” into price almost instantaneously. Such phenomena are studied in the field of market microstructure theory, which aims to explain and predict them. In this thesis, we model the dynamics of high frequency markets using non-linear hidden Markov models (HMMs). Such models feature an intuitive separation between observations and dynamics, and are therefore highly convenient tools in financial settings, where they allow a precise application of domain knowledge. HMMs can be formulated based on only a few parameters, yet their inherently dynamic nature can be used to capture well-known intra-day seasonality effects that many other models fail to explain. Due to recent breakthroughs in Monte Carlo methods, HMMs can now be efficiently estimated in real-time. In this thesis, we develop a holistic framework for performing both real-time inference and learning of HMMs, by combining several particle-based methods. Within this framework, we also provide methods for making accurate predictions from the model, as well as methods for assessing the model itself. In this framework, a sequential Monte Carlo bootstrap filter is adopted to make on-line inference and predictions. Coupled with a backward smoothing filter, this provides a forward filtering/backward smoothing scheme. This is then used in the sequential Monte Carlo expectation-maximization algorithm for finding the optimal hyper-parameters for the model. To design an HMM specifically for capturing information translation, we adopt the observable volume imbalance into a dynamic setting. Volume imbalance has previously been used in market microstructure theory to study, for example, price impact. Through careful selection of key model assumptions, we define a slightly modified observable as a process that we call scaled volume imbalance. The outcomes of this process retain the key features of volume imbalance (that is, its relationship to price impact and information), and allows an efficient evaluation of the framework, while providing a promising platform for future studies. This is demonstrated through a test on actual financial trading data, where we obtain high-performance predictions. Our results demonstrate that the proposed framework can successfully be applied to the field of market microstructure.
Under de senaste decennierna har det gjorts stora framsteg inom finansiell teori för kapitalmarknader. Formuleringen av arbitrageteori medförde möjligheten att konsekvent kunna prissätta finansiella instrument. Men i en tid då högfrekvenshandel numera är standard, har omsättningen av information i pris börjat ske i allt snabbare takt. För att studera dessa fenomen; prispåverkan och informationsomsättning, har mikrostrukturteorin vuxit fram. I den här uppsatsen studerar vi mikrostruktur med hjälp av en dynamisk modell. Historiskt sett har mikrostrukturteorin fokuserat på statiska modeller men med hjälp av icke-linjära dolda Markovmodeller (HMM:er) utökar vi detta till den dynamiska domänen. HMM:er kommer med en naturlig uppdelning mellan observation och dynamik, och är utformade på ett sådant sätt att vi kan dra nytta av domänspecifik kunskap. Genom att formulera lämpliga nyckelantaganden baserade på traditionell mikrostrukturteori specificerar vi en modell—med endast ett fåtal parametrar—som klarar av att beskriva de välkända säsongsbeteenden som statiska modeller inte klarar av. Tack vare nya genombrott inom Monte Carlo-metoder finns det nu kraftfulla verktyg att tillgå för att utföra optimal filtrering med HMM:er i realtid. Vi applicerar ett så kallat bootstrap filter för att sekventiellt filtrera fram tillståndet för modellen och prediktera framtida tillstånd. Tillsammans med tekniken backward smoothing estimerar vi den posteriora simultana fördelningen för varje handelsdag. Denna används sedan för statistisk inlärning av våra hyperparametrar via en sekventiell Monte Carlo Expectation Maximization-algoritm. För att formulera en modell som beskriver omsättningen av information, väljer vi att utgå ifrån volume imbalance, som ofta används för att studera prispåverkan. Vi definierar den relaterade observerbara storheten scaled volume imbalance som syftar till att bibehålla kopplingen till prispåverkan men även går att modellera med en dynamisk process som passar in i ramverket för HMM:er. Vi visar även hur man inom detta ramverk kan utvärdera HMM:er i allmänhet, samt genomför denna analys för vår modell i synnerhet. Modellen testas mot finansiell handelsdata för både terminskontrakt och aktier och visar i bägge fall god predikteringsförmåga.

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Bulla, Jan. „Computational Advances and Applications of Hidden (Semi-)Markov Models“. Habilitation à diriger des recherches, Université de Caen, 2013. http://tel.archives-ouvertes.fr/tel-00987183.

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The document is my habilitation thesis, which is a prerequisite for obtaining the "habilitation à diriger des recherche (HDR)" in France (https://fr.wikipedia.org/wiki/Habilitation_universitaire#En_France). The thesis is of cumulative form, thus providing an overview of my published works until summer 2013.

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Ghosh, Tusharkanti. „Hierarchical hidden Markov models with applications to BiSulfite-sequencing data“. Thesis, University of Glasgow, 2018. http://theses.gla.ac.uk/9036/.

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DNA methylation is an epigenetic modification with significant roles in various biological processes such as gene expression and cellular proliferation. Aberrant DNA methylation patterns compared to normal cells have been associated with a large number of human malignancies and potential cancer symptoms. In DNA methylation studies, an important objective is to detect differences between two groups under distinct biological conditions, for e.g., between cancer/ageing and normal cells. BiSulfite sequencing (BS-seq) is currently the gold standard for experimentally measuring genome-wide DNA methylation. Recent evolution in the BS-seq technologies enabled the DNA methylation profiles at single base pair resolution to be more accurate in terms of their genome coverages. The main objective of my thesis is to identify differential patterns of DNA methylation between proliferating and senescent cells. For efficient detection of differential methylation patterns, this thesis adopts the approach of Bayesian latent variable model. One such class of models is hidden Markov model (HMM) that can detect the underlying latent (hidden) structures of the model. In this thesis, I propose a family of Bayesian hierarchical HMMs for identifying differentially methylated cytosines (DMCs) and differentially methylated regions (DMRs) from BS-seq data which act as important indicators in better understanding of cancer and other related diseases. I introduce HMMmethState, a model-based hierarchical Bayesian technique for identifying DMCs from BS-seq data. My novel HMMmethState method implements hierarchical HMMs to account for spatial dependence among the CpG sites over genomic positions of BS-seq methylation data. In particular, this thesis is concerned with developing hierarchical HMMs for the differential methylation analysis of BS-seq data, within a Bayesian framework. In these models, aberrant DNA methylation is driven by two latent states: differentially methylated state and similarly methylated state, which can be interpreted as methylation status of CpG sites, that evolve over genomic positions as a first order Markov chain. I first design a (homogeneous) discrete-index hierarchical HMM in which methylated counts given the methylation status of CpG sites follow Beta-Binomial emission distribution specific to the methylation state. However, this model does not incorporate the genomic positional variations among the CpG sites, so I develop a (non-homogeneous) continuous-index hierarchical HMM, in which the transition probabilities between methylation status depend on the genomic positions of the CpG sites. This Beta-Binomial emission model however does not take into account the correlation in the methylated counts of the proliferating and senescent cells, which has been observed in the BS-seq data analysis. So, I develop a hierarchical Normal-logit Binomial emission model that induces correlation between the methylated counts of the proliferating and senescent cells. Furthermore, to perform parameter estimation for my models, I implement efficient Markov Chain Monte Carlo (MCMC) based algorithms. In this thesis, I provide an extensive study on model comparisons and adequacy of all the models using Bayesian model checking. In addition, I also show the performances of all the models using Receiver Operating Characteristics (ROC) curves. I illustrate the models by fitting them to a large BS-seq dataset and apply model selection criteria on the dataset in search of selecting the best model. In addition, I compare the performances of my methods with existing methods for detecting DMCs with competing methods. I demonstrate how the HMMmethState based algorithms outperform the existing methods in simulation studies in terms of ROC curves. I present the results of DMRs obtained using my method, i.e., the results of DMRs with the proposed HMMmethState that have been applied to the BS-seq datasets. The results of the hierarchical HMMs explain that I can certainly implement these methods under unconditioned settings to identify DMCs for high-throughput BS-seq data. The predicted DMCs can also help in understanding the phenotypic changes associated with human ageing.

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Vaicenavicius, Juozas. „Optimal Sequential Decisions in Hidden-State Models“. Doctoral thesis, Uppsala universitet, Matematiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-320809.

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This doctoral thesis consists of five research articles on the general topic of optimal decision making under uncertainty in a Bayesian framework. The papers are preceded by three introductory chapters. Papers I and II are dedicated to the problem of finding an optimal stopping strategy to liquidate an asset with unknown drift. In Paper I, the price is modelled by the classical Black-Scholes model with unknown drift. The first passage time of the posterior mean below a monotone boundary is shown to be optimal. The boundary is characterised as the unique solution to a nonlinear integral equation. Paper II solves the same optimal liquidation problem, but in a more general model with stochastic regime-switching volatility. An optimal liquidation strategy and various structural properties of the problem are determined. In Paper III, the problem of sequentially testing the sign of the drift of an arithmetic Brownian motion with the 0-1 loss function and a constant cost of observation per unit of time is studied from a Bayesian perspective. Optimal decision strategies for arbitrary prior distributions are determined and investigated. The strategies consist of two monotone stopping boundaries, which we characterise in terms of integral equations. In Paper IV, the problem of stopping a Brownian bridge with an unknown pinning point to maximise the expected value at the stopping time is studied. Besides a few general properties established, structural properties of an optimal strategy are shown to be sensitive to the prior. A general condition for a one-sided optimal stopping region is provided. Paper V deals with the problem of detecting a drift change of a Brownian motion under various extensions of the classical Wiener disorder problem. Monotonicity properties of the solution with respect to various model parameters are studied. Also, effects of a possible misspecification of the underlying model are explored.

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Lindberg, David Seaman III. „Enhancing Individualized Instruction through Hidden Markov Models“. The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1405350981.

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Dawson, Colin Reimer, und Colin Reimer Dawson. „HaMMLeT: An Infinite Hidden Markov Model with Local Transitions“. Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/626170.

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In classical mixture modeling, each data point is modeled as arising i.i.d. (typically) from a weighted sum of probability distributions. When data arises from different sources that may not give rise to the same mixture distribution, a hierarchical model can allow the source contexts (e.g., documents, sub-populations) to share components while assigning different weights across them (while perhaps coupling the weights to "borrow strength" across contexts). The Dirichlet Process (DP) Mixture Model (e.g., Rasmussen (2000)) is a Bayesian approach to mixture modeling which models the data as arising from a countably infinite number of components: the Dirichlet Process provides a prior on the mixture weights that guards against overfitting. The Hierarchical Dirichlet Process (HDP) Mixture Model (Teh et al., 2006) employs a separate DP Mixture Model for each context, but couples the weights across contexts. This coupling is critical to ensure that mixture components are reused across contexts. An important application of HDPs is to time series models, in particular Hidden Markov Models (HMMs), where the HDP can be used as a prior on a doubly infinite transition matrix for the latent Markov chain, giving rise to the HDP-HMM (first developed, as the "Infinite HMM", by Beal et al. (2001), and subsequently shown to be a case of an HDP by Teh et al. (2006)). There, the hierarchy is over rows of the transition matrix, and the distributions across rows are coupled through a top-level Dirichlet Process. In the first part of the dissertation, I present a formal overview of Mixture Models and Hidden Markov Models. I then turn to a discussion of Dirichlet Processes and their various representations, as well as associated schemes for tackling the problem of doing approximate inference over an infinitely flexible model with finite computa- tional resources. I will then turn to the Hierarchical Dirichlet Process (HDP) and its application to an infinite state Hidden Markov Model, the HDP-HMM. These models have been widely adopted in Bayesian statistics and machine learning. However, a limitation of the vanilla HDP is that it offers no mechanism to model correlations between mixture components across contexts. This is limiting in many applications, including topic modeling, where we expect certain components to occur or not occur together. In the HMM setting, we might expect certain states to exhibit similar incoming and outgoing transition probabilities; that is, for certain rows and columns of the transition matrix to be correlated. In particular, we might expect pairs of states that are "similar" in some way to transition frequently to each other. The HDP-HMM offers no mechanism to model this similarity structure. The central contribution of the dissertation is a novel generalization of the HDP- HMM which I call the Hierarchical Dirichlet Process Hidden Markov Model With Local Transitions (HDP-HMM-LT, or HaMMLeT for short), which allows for correlations between rows and columns of the transition matrix by assigning each state a location in a latent similarity space and promoting transitions between states that are near each other. I present a Gibbs sampling scheme for inference in this model, employing auxiliary variables to simplify the relevant conditional distributions, which have a natural interpretation after re-casting the discrete time Markov chain as a continuous time Markov Jump Process where holding times are integrated out, and where some jump attempts "fail". I refer to this novel representation as the Markov Process With Failed Jumps. I test this model on several synthetic and real data sets, showing that for data where transitions between similar states are more common, the HaMMLeT model more effectively finds the latent time series structure underlying the observations.

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McGillivray, Annaliza. „A penalized quasi-likelihood approach for estimating the number of states in a hidden markov model“. Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=110634.

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In statistical applications of hidden Markov models (HMMs), one may have no knowledge of the number of hidden states (or order) of the model needed to be able to accurately represent the underlying process of the data. The problem of estimating the number of states of the HMM is thus a task of major importance. We begin with a literature review of the major developments in the problem of order estimation for HMMs. We then propose a new penalized quasi-likelihood method for estimating the number of hidden states, which makes use of the fact that the marginal distribution of the HMM observations is a finite mixture model. Starting with a HMM with a large number of states, the method obtains a model of lower order by clustering and merging similar states of the model through two penalty functions. We study some of the asymptotic properties of the proposed method and present a numerical procedure for its implementation. The performance of the new method is assessed via extensive simulation studies for normal and Poisson HMMs. The new method is more computationally efficient than existing methods, such as AIC and BIC, as the order of the model is determined in a single optimization. We conclude with applications of the method to two real data sets.
Dans les applications des chaînes de Markov cachées (CMC), il se peut que les statisticiens n'aient pas l'information sur le nombre d'états (ou ordre) nécessaires pour représenter le processus. Le problème d'estimer le nombre d'états du CMC est ainsi une tâche d'importance majeure. Nous commençons avec une revue de littérature des développements majeurs dans le problème d'estimation de l'ordre d'un CMC. Nous proposons alors une nouvelle méthode de la quasi-vraisemblance pénalisée pour estimer l'ordre dans des CMC. Cette méthode utilise le fait que la distribution marginale des observations CMC est un mélange fini. La méthode débute avec un CMC avec un grand nombre d'états et obtient un modèle d'ordre inférieur en regroupant et fusionnant les états à l'aide de deux fonctions de pénalité. Nous étudions certaines propriétés asymptotiques de la méthode proposée et présentons une procédure numérique pour sa mise en œuvre. La performance est évaluée via des simulations extensives. La nouvelle méthode est plus efficace qu'autres méthodes, comme CIA et CIB, comme l'ordre du modèle est déterminé dans une seule optimisation. Nous concluons avec l'application de la méthode à deux vrais jeux de données.

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Bücher zum Thema "Hidden statistics"

Pete, Palmer, und Thorn John 1947-, Hrsg. The hidden game of football. New York, NY: Warner Books, 1988.

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Shire, David. Half truths, Half measures: Hidden statistics on black unemployment. London): Black Employment Institute, 1997.

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Suicide: The hidden side of modernity. Cambridge, UK: Polity Press, 2008.

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Bob, Carroll. The hidden game of football: The next edition. New York: Total Sports, 1998.

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Zucchini, W. Hidden Markov models for time series: An introduction using R. Boca Raton: Chapman & Hall/CRC, 2009.

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John, Thorn. The hidden game of baseball: A revolutionary approach to baseball and its statistics. Garden City, N.Y: Doubleday, 1985.

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Viavant, Timothy Roland. Observations of fish attraction devices in Hidden and Harding Lakes, Alaska. Anchorage: Alaska Dept. of Fish and Game, Division of Sport Fish, Research and Technical Services, 1996.

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Numbers rule your world: The hidden influence of probability and statistics on everything you do. New York: McGraw-Hill, 2010.

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Numbers rule your world: The hidden influence of probabilities and statistics on everything you do. New York: McGraw-Hill, 2010.

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Numbers rule your world: The hidden influence of probabilities and statistics on everything you do. New York: McGraw-Hill, 2010.

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Buchteile zum Thema "Hidden statistics"

Flajolet, Philippe, Yves Guivarc’h, Wojciech Szpankowski und Brigitte Vallée. „Hidden Pattern Statistics“. In Automata, Languages and Programming, 152–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48224-5_13.

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Rosenbaum, Paul R. „Sensitivity to Hidden Bias“. In Springer Series in Statistics, 105–70. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4757-3692-2_4.

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Rosenbaum, Paul R. „Sensitivity to Hidden Bias“. In Springer Series in Statistics, 87–135. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4757-2443-1_4.

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Chopin, Nicolas, und 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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Forsyth, David. „Markov Chains and Hidden Markov Models“. In Probability and Statistics for Computer Science, 331–51. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64410-3_14.

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Salvador, Paulo, António Nogueira und Eduardo Rocha. „Multiscale Internet Statistics: Unveiling the Hidden Behavior“. In CIM Series in Mathematical Sciences, 317–38. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16121-1_15.

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Andersen, Lars Smedegaard. „Inference for hidden Markov models“. In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 1–13. Hayward, CA: Institute of Mathematical Statistics, 1991. http://dx.doi.org/10.1214/lnms/1215460489.

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Kobayashi, Toshiyuki, und Birgit Speh. „A Hidden Symmetry of a Branching Law“. In Springer Proceedings in Mathematics & Statistics, 15–28. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7775-8_2.

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Vernet, Elodie. „Consistency of Bayesian Nonparametric Hidden Markov Models“. In The Contribution of Young Researchers to Bayesian Statistics, 41–43. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02084-6_9.

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Ghosh, Indranil, und Hon Keung Tony Ng. „Hidden Truncation in Non-Normal Models: A Brief Survey“. In Advances in Statistics - Theory and Applications, 133–60. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62900-7_7.

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Konferenzberichte zum Thema "Hidden statistics"

Aboulnaga, Ashraf, und Jeffrey F. Naughton. „Building XML statistics for the hidden web“. In the twelfth international conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/956863.956930.

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Loesing, Karsten, Werner Sandmann, Christian Wilms und Guido Wirtz. „Performance Measurements and Statistics of Tor Hidden Services“. In 2008 International Symposium on Applications and the Internet. IEEE, 2008. http://dx.doi.org/10.1109/saint.2008.69.

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ARNAB, RAGHUNATH, und SARJINDER SINGH. „ON THE ESTIMATION OF SIZE AND MEAN VALUE OF A STIGMATIZED CHARACTERISTIC OF A HIDDEN GANG IN A FINITE POPULATION“. In Proceedings of Statistics 2001 Canada: The 4th Conference in Applied Statistics. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2002. http://dx.doi.org/10.1142/9781860949531_0001.

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Glusman, Gustavo. „Mining “junk” DNA to find hidden transcriptional gems“. In 2009 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2009. http://dx.doi.org/10.1109/gensips.2009.5174348.

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Mallek, Maha, Ramzi Guetari, Nejmeddine Etteyeb und Walid Ghariani. „Graphical representation of statistics hidden in unstructured data: A software application“. In 2017 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2017. http://dx.doi.org/10.1109/smc.2017.8122681.

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Huang, Huajun, Shaohong Zhong und Xingming Sun. „Steganalysis of Information Hidden in Webpage Based on Higher-order Statistics“. In 2008 International Symposium on Electronic Commerce and Security. IEEE, 2008. http://dx.doi.org/10.1109/isecs.2008.169.

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Deka, Deepjyoti, Michael Chertkov und Scott Backhaus. „Estimating topology and injection statistics in distribution grids with hidden nodes“. In 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2017. http://dx.doi.org/10.1109/smartgridcomm.2017.8340739.

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Ramadhan, Rahmawati, Dodi Devianto und Maiyastri Maiyastri. „Hidden Markov Model for Exchange Rate with EWMA Control Chart“. In Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia. EAI, 2020. http://dx.doi.org/10.4108/eai.2-8-2019.2290474.

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Lukashenko, Oleg, und Sergey Chernyaev. „FMRI image segmentation based on hidden Markov random field with directional statistics observation model“. In Twelfth International Conference on Machine Vision, herausgegeben von Wolfgang Osten und Dmitry P. Nikolaev. SPIE, 2020. http://dx.doi.org/10.1117/12.2559545.

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Martin, Donald E. K., John A. D. Aston und Sio-Iong Ao. „Exact Distribution Of Statistics Of Hidden State Sequences Via Message Passing in Factor Graphs“. In IAENG TRANSACTIONS ON ENGINEERING TECHNOLOGIES VOLUME 2: Special Edition of the World Congress on Engineering and Computer Science. AIP, 2009. http://dx.doi.org/10.1063/1.3146186.

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Berichte der Organisationen zum Thema "Hidden statistics"

Doleac, Jennifer, und Benjamin Hansen. Does “Ban the Box” Help or Hurt Low-Skilled Workers? Statistical Discrimination and Employment Outcomes When Criminal Histories are Hidden. Cambridge, MA: National Bureau of Economic Research, Juli 2016. http://dx.doi.org/10.3386/w22469.

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Perdigão, Rui A. P., und Julia Hall. Spatiotemporal Causality and Predictability Beyond Recurrence Collapse in Complex Coevolutionary Systems. Meteoceanics, November 2020. http://dx.doi.org/10.46337/201111.

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Causality and Predictability of Complex Systems pose fundamental challenges even under well-defined structural stochastic-dynamic conditions where the laws of motion and system symmetries are known. However, the edifice of complexity can be profoundly transformed by structural-functional coevolution and non-recurrent elusive mechanisms changing the very same invariants of motion that had been taken for granted. This leads to recurrence collapse and memory loss, precluding the ability of traditional stochastic-dynamic and information-theoretic metrics to provide reliable information about the non-recurrent emergence of fundamental new properties absent from the a priori kinematic geometric and statistical features. Unveiling causal mechanisms and eliciting system dynamic predictability under such challenging conditions is not only a fundamental problem in mathematical and statistical physics, but also one of critical importance to dynamic modelling, risk assessment and decision support e.g. regarding non-recurrent critical transitions and extreme events. In order to address these challenges, generalized metrics in non-ergodic information physics are hereby introduced for unveiling elusive dynamics, causality and predictability of complex dynamical systems undergoing far-from-equilibrium structural-functional coevolution. With these methodological developments at hand, hidden dynamic information is hereby brought out and explicitly quantified even beyond post-critical regime collapse, long after statistical information is lost. The added causal insights and operational predictive value are further highlighted by evaluating the new information metrics among statistically independent variables, where traditional techniques therefore find no information links. Notwithstanding the factorability of the distributions associated to the aforementioned independent variables, synergistic and redundant information are found to emerge from microphysical, event-scale codependencies in far-from-equilibrium nonlinear statistical mechanics. The findings are illustrated to shed light onto fundamental causal mechanisms and unveil elusive dynamic predictability of non-recurrent critical transitions and extreme events across multiscale hydro-climatic problems.

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