Search results for " Marko"

showing 10 items of 201 documents

Analysis of clickstream data with mixture hidden markov models

2021

clickstream data sono un’importante fonte di informazioni per l’ecommerce, sebbene non siano semplici da gestire e convertire queste informazioni in un reale vantaggio competitivo non e un compito banale. In questo articolo, consid- ` eriamo l’applicazione dei mixture hidden Markov model a dati relativi al flusso di clickstream estratti dal portale e-commerce di un’azienda di servizi turistici. Sono stati individuati cluster relativi al comportamento di navigazione degli utenti e alla loro posizione geografica che forniscono indicazioni importanti per lo sviluppo di nuove strategie di business. Clickstream data is an important source of information for businesses, however it is not easy to …

Settore SECS-S/03 - Statistica EconomicaClickstream Data Online browsing behaviour Mixture hidden Markov models Tourism 2.0 Web mining
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Observer-based finite-time fuzzy H∞ control for discrete-time systems with stochastic jumps and time-delays

2014

This paper is concerned with the problem of observer-based finite-time H ∞ control for a family of discrete-time Markovian jump nonlinear systems with time-delays represented by Takagi-Sugeno (T-S) model. The main contribution of this paper is to design an observer-based finite-time H ∞ controller such that the resulting closed-loop system is stochastic finite-time bounded and satisfies a prescribed H ∞ disturbance attenuation level over the given finite-time interval. Sufficient criteria on stochastic finite-time H ∞ stabilization via observer-based fuzzy state feedback are presented for the solvability of the problem, which can be tackled by a feasibility problem in terms of linear matrix…

Signal processingObserver (quantum physics)Finite-time H∞ controlTakagi-Sugeno (T-S) modelMarkovian jump systemsFuzzy control systemFuzzy logicFinite-time H∞ control; Markovian jump systems; Observer-based control; Takagi-Sugeno (T-S) model; Electrical and Electronic Engineering; Control and Systems Engineering; Software; Signal Processing; 1707Nonlinear systemobserver-based controlTakagi–Sugeno (T–S) modelDiscrete time and continuous timeControl and Systems EngineeringControl theoryBounded functionSignal ProcessingComputer Vision and Pattern RecognitionState observerElectrical and Electronic Engineeringfinite-time H∞ controlfinite-time H infinity controlObserver-based controlSoftware1707Mathematics
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Una metodologia per la suddivisione di un'area geografica regionale in Sistemi Turistici Locali.

2005

In questo lavoro si propone un metodo per a®rontare e risolvere il problema del dimensionamento e della localizzazione ottimali dei Sistemi Turistici Locali. A tal ¯ne, l'applicazione del modello proposto sull'area territoriale in esame viene iservata ad una fase in cui sono stati giµa presi in considerazione opzioni e vincoli trategici; viene presentato, invero, un criterio per la de¯nizione della gerarchia delle unitµa territoriali, che consente, d'altra parte, una sensibile riduzione delle alterna- ive ammissibili nella de¯nizione dei sistemi turistici, che avviene proponendo uno peci¯co algoritmo. l problema di ottimizzazione µe risolto successivamente mediante un procedimento di ricerc…

Sistemi Turistici Locali Programmazione Dinamica Alberi Decisionali Catene di Markov
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Towards a fuzzy-linguistic based social network sentiment-expression system

2015

Liking allows users of Social Networks, blogs and online magazines to express their support of posts and artifacts by a simple click. Such function is very popular but lacks semantic power, and some platforms have augmented it by allowing to choose a pictographic depiction corresponding to a feeling. What is gained in depth is lost in simplicity, and the wide acceptance liking has enjoyed did not carried to the sentiment version. We outline a sentiment-expression hybrid system based on textual analysis and linguistic fuzzy Markov chains overcoming the intrinsic limitations of liking without burdening the user with complex choices.

Social networkSettore INF/01 - Informaticabusiness.industryComputer scienceSentiment analysisSettore M-FIL/02 - Logica E Filosofia Della Scienzacomputer.software_genresocial networks sentiment analysis linguis- tic fuzzy Markov chainsExpression (architecture)Fuzzy linguisticArtificial intelligencebusinesscomputerNatural language processing
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Usage of HMM-Based Speech Recognition Methods for Automated Determination of a Similarity Level Between Languages

2019

The problem of automated determination of language similarity (or even defining of a distance on the space of languages) could be solved in different ways – working with phonetic transcriptions, with speech recordings or both of them. For the recordings, we propose and test a HMM-based one: in the first part of our article we successfully try language detection, afterwards we are trying to calculate distances between HMM-based models, using different metrics and divergences. The Kullback-Leibler divergence is the only one we got good results with – it means that the calculated distances between languages correspond to analytical understanding of similarity between them. Even if it does not …

Space (punctuation)Kullback–Leibler divergenceLanguage identificationSimilarity (network science)Computer scienceSpeech recognitionComputer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)Hidden Markov modelUSableDivergence (statistics)
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On Hagelbarger’s and Shannon’s matching pennies playing machines

2020

Abstract In the 1950s, Hagelbarger’s Sequence Extrapolating Robot (SEER) and Shannon’s Mind-Reading Machine (MRM) were the state-of-the-art research results in playing the well-known “matching pennies” game. In our research we perform a software implementation for both machines in order to test the common statement that MRM, even simpler, beats SEER. Also, we propose a simple contextual predictor (SCP) and use it to compete with SEER and MRM. As expected, experimental results proves the claimed MRM superiority over SEER and even the SCP’s superiority over both SEER and MRM. At the end, we draw some conclusions and propose further research ideas, like the use of mixing models methods and the…

Statement (computer science)Sequencebusiness.industryComputer scienceRobotArtificial intelligenceHidden Markov modelMatching penniesbusinessSoftware implementationInternational Journal of Advanced Statistics and IT&C for Economics and Life Sciences
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Bayesian hierarchical Poisson models with a hidden Markov structure for the detection of influenza epidemic outbreaks

2015

Considerable effort has been devoted to the development of statistical algorithms for the automated monitoring of influenza surveillance data. In this article, we introduce a framework of models for the early detection of the onset of an influenza epidemic which is applicable to different kinds of surveillance data. In particular, the process of the observed cases is modelled via a Bayesian Hierarchical Poisson model in which the intensity parameter is a function of the incidence rate. The key point is to consider this incidence rate as a normal distribution in which both parameters (mean and variance) are modelled differently, depending on whether the system is in an epidemic or non-epide…

Statistics and ProbabilityEpidemiologyComputer scienceBayesian probabilityBiostatisticsPoisson distributionBayesian inferenceDisease OutbreaksNormal distributionsymbols.namesakeHealth Information ManagementInfluenza HumanStatisticsEconometricsHumansPoisson DistributionPoisson regressionEpidemicsHidden Markov modelProbabilityInternetModels StatisticalIncidenceBayes TheoremMarkov ChainsSearch EngineMoment (mathematics)Autoregressive modelSpainsymbolsMonte Carlo MethodSentinel Surveillance
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Bayesian Markov switching models for the early detection of influenza epidemics

2008

The early detection of outbreaks of diseases is one of the most challenging objectives of epidemiological surveillance systems. In this paper, a Markov switching model is introduced to determine the epidemic and non-epidemic periods from influenza surveillance data: the process of differenced incidence rates is modelled either with a first-order autoregressive process or with a Gaussian white-noise process depending on whether the system is in an epidemic or in a non-epidemic phase. The transition between phases of the disease is modelled as a Markovian process. Bayesian inference is carried out on the former model to detect influenza epidemics at the very moment of their onset. Moreover, t…

Statistics and ProbabilityEpidemiologyComputer scienceBayesian probabilityMarkov processBayesian inferenceDisease Outbreakssymbols.namesakeBayes' theoremStatisticsInfluenza HumanEconometricsHumansHidden Markov modelModels StatisticalMarkov chainIncidenceBayes TheoremMarkov ChainsMoment (mathematics)Autoregressive modelSpainSpace-Time ClusteringsymbolsRegression AnalysisSentinel Surveillance
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Multi-Phase epidemic model by a Markov chain

2008

Abstract In this paper we propose a continuous-time Markov chain to describe the spread of an infective and non-mortal disease into a community numerically limited and subjected to an external infection. We make a numerical simulation that shows tendencies for recurring epidemic outbreaks and for fade-out or extinction of the infection.

Statistics and ProbabilityExtinctionMarkov chainMulti phaseComputer scienceEpidemic models Markov chain Numerical simulationStatistical physicsCondensed Matter PhysicsEpidemic model
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Can the Adaptive Metropolis Algorithm Collapse Without the Covariance Lower Bound?

2011

The Adaptive Metropolis (AM) algorithm is based on the symmetric random-walk Metropolis algorithm. The proposal distribution has the following time-dependent covariance matrix at step $n+1$ \[ S_n = Cov(X_1,...,X_n) + \epsilon I, \] that is, the sample covariance matrix of the history of the chain plus a (small) constant $\epsilon>0$ multiple of the identity matrix $I$. The lower bound on the eigenvalues of $S_n$ induced by the factor $\epsilon I$ is theoretically convenient, but practically cumbersome, as a good value for the parameter $\epsilon$ may not always be easy to choose. This article considers variants of the AM algorithm that do not explicitly bound the eigenvalues of $S_n$ away …

Statistics and ProbabilityFOS: Computer and information sciencesIdentity matrixMathematics - Statistics TheoryStatistics Theory (math.ST)Upper and lower boundsStatistics - Computation93E3593E15Combinatorics60J27Mathematics::ProbabilityLaw of large numbers65C40 60J27 93E15 93E35stochastic approximationFOS: MathematicsEigenvalues and eigenvectorsComputation (stat.CO)Metropolis algorithmMathematicsProbability (math.PR)Zero (complex analysis)CovariancestabilityUniform continuityBounded function65C40Statistics Probability and Uncertaintyadaptive Markov chain Monte CarloMathematics - Probability
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