Search results for "Markov"

showing 10 items of 628 documents

Forecasting correlated time series with exponential smoothing models

2011

Abstract This paper presents the Bayesian analysis of a general multivariate exponential smoothing model that allows us to forecast time series jointly, subject to correlated random disturbances. The general multivariate model, which can be formulated as a seemingly unrelated regression model, includes the previously studied homogeneous multivariate Holt-Winters’ model as a special case when all of the univariate series share a common structure. MCMC simulation techniques are required in order to approach the non-analytically tractable posterior distribution of the model parameters. The predictive distribution is then estimated using Monte Carlo integration. A Bayesian model selection crite…

Multivariate statisticsMathematical optimizationsymbols.namesakeModel selectionExponential smoothingPosterior probabilitysymbolsUnivariateMarkov chain Monte CarloBusiness and International ManagementSeemingly unrelated regressionsBayesian inferenceMathematicsInternational Journal of Forecasting
researchProduct

Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer

2019

AbstractA spatial Markov-chain model is formulated for the progression of skin cancer. The model is based on the division of the computational domain into nodal points, that can be in a binary state: either in ‘cancer state’ or in ‘non-cancer state’. The model assigns probabilities for the non-reversible transition from ‘non-cancer’ state to the ‘cancer state’ that depend on the states of the neighbouring nodes. The likelihood of transition further depends on the life burden intensity of the UV-rays that the skin is exposed to. The probabilistic nature of the process and the uncertainty in the input data is assessed by the use of Monte Carlo simulations. A good fit between experiments on mi…

65C05Skin NeoplasmsComputer scienceQuantitative Biology::Tissues and OrgansMarkovin ketjut0206 medical engineeringMonte Carlo methodPhysics::Medical PhysicsBinary number02 engineering and technologyArticleihosyöpä03 medical and health sciencesMicemedicineAnimalsHumansComputer SimulationStatistical physicsUncertainty quantification60J20stokastiset prosessit030304 developmental biologyProbability0303 health sciencesMarkov chainApplied MathematicsProbabilistic logicUncertaintyState (functional analysis)medicine.disease020601 biomedical engineeringAgricultural and Biological Sciences (miscellaneous)Markov ChainsCardinal pointModeling and Simulation65C40Disease Progressionmatemaattiset mallitSkin cancerMonte Carlo MethodJournal of Mathematical Biology
researchProduct

Robust non-Markovianity in ultracold gases

2012

We study the effect of thermal fluctuations on a probe qubit interacting with a Bose-Einstein condensed (BEC) reservoir. The zero-temperature case was studied in [Haikka P et al 2011 Phys. Rev. A 84 031602], where we proposed a method to probe the effects of dimensionality and scattering length of a BEC based on its behavior as an environment. Here we show that the sensitivity of the probe qubit is remarkably robust against thermal noise. We give an intuitive explanation for the thermal resilience, showing that it is due to the unique choice of the probe qubit architecture of our model.

PhysicsCondensed Matter::Quantum GasesWork (thermodynamics)Quantum PhysicsCold Atoms Open Quantum System Markovian Master equations/dk/atira/pure/subjectarea/asjc/3100/3107/dk/atira/pure/subjectarea/asjc/3100/3104Thermal fluctuationsFOS: Physical sciencesScattering lengthPhysics and Astronomy(all)Condensed Matter PhysicsSettore FIS/03 - Fisica Della MateriaAtomic and Molecular Physics and Optics/dk/atira/pure/subjectarea/asjc/3100Quantum Gases (cond-mat.quant-gas)Quantum mechanicsQubitThermalSensitivity (control systems)Condensed Matter - Quantum Gases/dk/atira/pure/subjectarea/asjc/2600/2610Quantum Physics (quant-ph)Mathematical PhysicsCurse of dimensionality
researchProduct

Hidden Markov Random Fields and Direct Search Methods for Medical Image Segmentation

2016

The goal of image segmentation is to simplify the representation of an image to items meaningful and easier to analyze. Medical image segmentation is one of the fundamental problems in image processing field. It aims to provide a crucial decision support to physicians. There is no one way to perform the segmentation. There are several methods based on HMRF. Hidden Markov Random Fields (HMRF) constitute an elegant way to model the problem of segmentation. This modelling leads to the minimization of an energy function. In this paper we investigate direct search methods that are Nelder-Mead and Torczon methods to solve this optimization problem. The quality of segmentation is evaluated on grou…

Segmentation-based object categorizationbusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationImage processing02 engineering and technologyImage segmentationMachine learningcomputer.software_genreSørensen–Dice coefficient0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingSegmentationArtificial intelligenceHidden Markov random fieldHidden Markov modelbusinesscomputerMathematicsProceedings of the 5th International Conference on Pattern Recognition Applications and Methods
researchProduct

Stability analysis for stochastic hybrid systems: A survey

2014

This survey addresses stability analysis for stochastic hybrid systems (SHS), which are dynamical systems that combine continuous change and instantaneous change and that also include random effects. We re-emphasize the common features found in most of the models that have appeared in the literature, which include stochastic switched systems, Markov jump systems, impulsive stochastic systems, switching diffusions, stochastic impulsive systems driven by renewal processes, diffusions driven by Lévy processes, piecewise-deterministic Markov processes, general stochastic hybrid systems, and stochastic hybrid inclusions. Then we review many of the stability concepts that have been studied, inclu…

Lyapunov functionLyapunov stabilityContinuous-time stochastic processLyapunov functionDynamical systems theoryStochastic differential equationMarkov chainStochastic stabilityConverse theoremStochastic hybrid systemsymbols.namesakeStochastic differential equationSettore ING-INF/04 - AutomaticaControl and Systems EngineeringControl theoryHybrid systemStability theorysymbolsSwitching diffusionStochastic optimizationElectrical and Electronic EngineeringRobustnessStochastic switched systemMathematics
researchProduct

Learning from Errors: Detecting ZigBee Interference in WiFi Networks

2014

In this work we show how to detect ZigBee interference on commodity WiFi cards by monitoring the reception errors, such as synchronization errors, invalid header formats, too long frames, etc., caused by ZigBee transmissions. Indeed, in presence of non-WiFi modulated signals, the occurrence of these types of errors follows statistics that can be easily recognized. Moreover, the duration of the error bursts depends on the transmission interval of the interference source, while the error spacing depends on the receiver implementation. On the basis of these considerations, we propose the adoption of hidden Markov chains for characterizing the behavior of WiFi receivers in presence of controlle…

business.industryComputer scienceSettore ING-INF/03 - TelecomunicazioniComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKSReal-time computingwlan 802.11 802.15.4 frame error detection wireless coexistenceInterval (mathematics)Interference (wave propagation)SynchronizationLearning from errorsTransmission (telecommunications)HeaderbusinessHidden Markov modelComputer networkNeuRFon
researchProduct

Self-stabilizing Balls & Bins in Batches

2016

A fundamental problem in distributed computing is the distribution of requests to a set of uniform servers without a centralized controller. Classically, such problems are modelled as static balls into bins processes, where m balls (tasks) are to be distributed to n bins (servers). In a seminal work, [Azar et al.; JoC'99] proposed the sequential strategy Greedy[d] for n = m. When thrown, a ball queries the load of d random bins and is allocated to a least loaded of these. [Azar et al.; JoC'99] showed that d=2 yields an exponential improvement compared to d=1. [Berenbrink et al.; JoC'06] extended this to m ⇒ n, showing that the maximal load difference is independent of m for d=2 (in contrast…

Mathematical optimizationMarkov chainSelf-stabilization0102 computer and information sciencesNew variantExpected value01 natural sciencesBinExponential functionCombinatorics010104 statistics & probability010201 computation theory & mathematicsTheoryofComputation_ANALYSISOFALGORITHMSANDPROBLEMCOMPLEXITYServerBall (bearing)0101 mathematicsMathematicsProceedings of the 2016 ACM Symposium on Principles of Distributed Computing
researchProduct

Robust Assembly Assistance Using Informed Tree Search with Markov Chains

2022

Manual work accounts for one of the largest workgroups in the European manufacturing sector, and improving the training capacity, quality, and speed brings significant competitive benefits to companies. In this context, this paper presents an informed tree search on top of a Markov chain that suggests possible next assembly steps as a key component of an innovative assembly training station for manual operations. The goal of the next step suggestions is to provide support to inexperienced workers or to assist experienced workers by providing choices for the next assembly step in an automated manner without the involvement of a human trainer on site. Data stemming from 179 experiment partici…

Markov chainsChemical technologytraining stationsTP1-1185predictionIndustry 4.0artificial intelligenceBiochemistryArticleAtomic and Molecular Physics and OpticsAnalytical ChemistryA* algorithmassembly assistance systems; training stations; smart manufacturing; Industry 4.0; digital transformation; informed tree search; A* algorithm; Markov chains; prediction; artificial intelligenceinformed tree searchHumansdigital transformationassembly assistance systemssmart manufacturingElectrical and Electronic EngineeringInstrumentationAlgorithmsSensors; Volume 22; Issue 2; Pages: 495
researchProduct

A Novel Method to Characterize User Sessions of Educational Software

2013

Abstract Software applications destined for the educational environment have a long history and have evolved side by side with the progress of technology from simple computer assisted instruction programs to sophisticated eLearning platforms. A study that we have conducted on a sample of 395 children aged 6 through 12, coming from both the rural and the urban environments, shows that an increasing number of children use computer related technologies. Given their exposure to these technologies it is imperative that the educational applications be designed in a way that takes into account the children's abilities, interests and the demands for their development. We have proposed a 5-dimension…

Markov chainMultimediaPoint (typography)User actions modellingbusiness.industryComputer scienceComputer-Assisted InstructionComputer user satisfactionSample (statistics)Markov modelcomputer.software_genreMarkov modelSoftwareHuman–computer interactionEducational softwareeLearningGeneral Materials SciencebusinesscomputerEducational softwareProcedia - Social and Behavioral Sciences
researchProduct

A cubic defining algebra for the Links–Gould polynomial

2013

Abstract We define a finite-dimensional cubic quotient of the group algebra of the braid group, endowed with a (essentially unique) Markov trace which affords the Links–Gould invariant of knots and links. We investigate several of its properties, and state several conjectures about its structure.

Essentially uniqueAlgebraMarkov chainGeneral MathematicsBraid groupGroup algebraBraid theoryInvariant (mathematics)Mathematics::Geometric TopologyQuotientMathematicsAdvances in Mathematics
researchProduct