Search results for " Probability"

showing 10 items of 2176 documents

Using plot soil loss distribution for soil conservation design

2011

Abstract Soil conservation design is generally based on the estimation of average annual soil loss but it should be developed taking into account storms of a given return period. However, use of frequency analysis in soil erosion studies is relatively limited. In this paper, an investigation on statistical distribution of soil loss measurements was firstly carried out using a relatively high number of simultaneously operating plots of different lengths, λ (11, 22, 33 and 44 m) at the experimental station of Sparacia (southern Italy). Using a simple normalization technique, the analysis showed that the probability distribution of the normalized soil loss is independent of both the scale leng…

Return periodHydrologyNormalization (statistics)Frequency analysisSoil erosion USLE probability distributions soil conservation practices designSoil scienceStormlaw.inventionSoil losslawEnvironmental scienceProbability distributionSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliSoil conservationEarth-Surface Processes
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Shrinkage efficiency bounds: An extension

2023

Hansen (2005) obtained the efficiency bound (the lowest achievable risk) in the p-dimensional normal location model when p≥3, generalizing an earlier result of Magnus (2002) for the one-dimensional case (p=1). The classes of estimators considered are, however, different in the two cases. We provide an alternative bound to Hansen's which is a more natural generalization of the one-dimensional case, and we compare the classes and the bounds.

RiskStatistics and ProbabilityLower boundSettore SECS-P/05 - EconometriaShrinkage estimatorNormal location modelCommunications in Statistics - Theory and Methods
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Bayesian joint modeling of bivariate longitudinal and competing risks data: An application to study patient-ventilator asynchronies in critical care …

2017

Mechanical ventilation is a common procedure of life support in intensive care. Patient-ventilator asynchronies (PVAs) occur when the timing of the ventilator cycle is not simultaneous with the timing of the patient respiratory cycle. The association between severity markers and the events death or alive discharge has been acknowledged before, however, little is known about the addition of PVAs data to the analyses. We used an index of asynchronies (AI) to measure PVAs and the SOFA (sequential organ failure assessment) score to assess overall severity. To investigate the added value of including the AI, we propose a Bayesian joint model of bivariate longitudinal and competing risks data. Th…

RiskStatistics and ProbabilityMixed modelmedicine.medical_specialtyBiometryCritical Caremedicine.medical_treatmentBayesian probabilityBivariate analysisCompeting risks01 natural sciences010104 statistics & probability03 medical and health sciences0302 clinical medicineIntensive careStatisticsmedicineHumansLongitudinal Studies0101 mathematicsMechanical ventilationModels Statisticalbusiness.industryRespirationBayes TheoremGeneral MedicineRespiration Artificial030228 respiratory systemLife supportEmergency medicineSOFA scoreStatistics Probability and UncertaintybusinessBiometrical Journal
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"Table 3" of "Measurement of the $W+b$-jet and $W+c$-jet differential production cross sections in $p\bar{p}$ collisions at $\sqrt{s}=1.96$ TeV"

2016

The $\sigma(W+c)/\sigma(W+b)$ cross section ratio in bins of $c(b)$-jet $p_T$.

SIG/SIGAstrophysics::High Energy Astrophysical PhenomenaIntegrated Cross SectionPBAR P --> W- CJET XAstrophysics::Cosmology and Extragalactic AstrophysicsJet ProductionPhysics::Data Analysis; Statistics and ProbabilityCross SectionPBAR P --> W+ CJET XInclusivePBAR P --> W+ BJET XPBAR P --> W- BJET XHigh Energy Physics::ExperimentNuclear Experiment1960.0W Production
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"Table 2" of "Measurement of the differential photon+ c-jet cross section and the ratio of differential photon+ c and photon+ b cross sections in pro…

2013

The ratio of the (GAMMA+ CJET) to (GAMMA+ BJET) cross section in bins of the GAMMA PT.

SIG/SIGAstrophysics::High Energy Astrophysical PhenomenaeducationIntegrated Cross SectionPBAR P --> GAMMA BJET Xfood and beveragesAstrophysics::Cosmology and Extragalactic AstrophysicsJet ProductionPhysics::Data Analysis; Statistics and ProbabilityPBAR P --> GAMMA CJET XCross Sectionbody regionsInclusivenatural sciences1960.0
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Nonsymmetric conical upper density and $k$-porosity

2017

We study how the Hausdorff measure is distributed in nonsymmetric narrow cones in R n \mathbb {R}^n . As an application, we find an upper bound close to n − k n-k for the Hausdorff dimension of sets with large k k -porosity. With k k -porous sets we mean sets which have holes in k k different directions on every small scale.

Scale (ratio)Applied MathematicsGeneral Mathematics010102 general mathematicsMathematicsofComputing_GENERALGeometryConical surface01 natural sciencesUpper and lower bounds010104 statistics & probabilityMathematics - Classical Analysis and ODEsHausdorff dimensionClassical Analysis and ODEs (math.CA)FOS: MathematicsHausdorff measure0101 mathematicsPorosityMathematics
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Towards a mean body for apparel design

2016

This paper focuses on shape average with applications to the apparel industry. Apparel industry uses a consensus sizing system; its major concern is to fit most of the population into it. Since anthropometric measures do not grow linearly, it is important to find prototypes to accurately represent each size. This is done using random compact mean sets, obtained from a cloud of 3D points given by a scanner and applying to the sample a previous definition of mean set. Additionally, two approaches to define confidence sets are introduced. The methodology is applied to data obtained from a real anthropometric survey. This paper has been partially supported by the following grants: TIN2009-14392…

ScannerComputer sciencePopulationCloud computingSample (statistics)02 engineering and technologycomputer.software_genre01 natural sciencesSet (abstract data type)010104 statistics & probabilityMean setAnthropometric surveyApparel design0202 electrical engineering electronic engineering information engineering0101 mathematicseducationeducation.field_of_studybusiness.industryConfidence setClothingSizingCompact spaceSignal Processing020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionData miningbusinesscomputerImage and Vision Computing
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Combining hashing and enciphering algorithms for epidemiological analysis of gathered data.

2008

Summary Objectives: Compiling individual records coming from different sources is necessary for multi-center studies. Legal aspects can be satisfied by implementing anonymization procedures. When using these procedures with a different key for each study it becomes almost impossible to link records from separate data collections. Methods: The originality of the method relies on the way the combination of hashing and enciphering techniques is performed: like in asymmetric encryption, two keys are used but the private key depends on the patient’s identity. Results: The combination of hashing and enciphering techniques provides a great improvement in the overall security of the proposed scheme…

Scheme (programming language)Computer sciencemedia_common.quotation_subjectHash functionHealth Informaticscomputer.software_genreEncryption01 natural sciencesField (computer science)Patient identificationPublic-key cryptography010104 statistics & probability03 medical and health sciences0302 clinical medicineHealth Information ManagementOriginality030212 general & internal medicine0101 mathematicscomputer.programming_languagemedia_commonAdvanced and Specialized Nursingbusiness.industryData CollectionEpidemiologic StudiesIdentity (object-oriented programming)Data miningbusinesscomputerAlgorithmsConfidentialityMethods of information in medicine
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“Anti-Bayesian” flat and hierarchical clustering using symmetric quantiloids

2017

A myriad of works has been published for achieving data clustering based on the Bayesian paradigm, where the clustering sometimes resorts to Naive-Bayes decisions. Within the domain of clustering, the Bayesian principle corresponds to assigning the unlabelled samples to the cluster whose mean (or centroid) is the closest. Recently, Oommen and his co-authors have proposed a novel, counter-intuitive and pioneering PR scheme that is radically opposed to the Bayesian principle. The rational for this paradigm, referred to as the “Anti-Bayesian” (AB) paradigm, involves classification based on the non-central quantiles of the distributions. The first-reported work to achieve clustering using the A…

Scheme (programming language)Information Systems and ManagementTheoretical computer scienceComputer scienceBayesian principleBayesian probabilityVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410::Statistikk: 412Multivariate normal distribution0102 computer and information sciences02 engineering and technology01 natural sciencesDomain (mathematical analysis)ClusteringTheoretical Computer ScienceArtificial Intelligence0103 physical sciencesCluster (physics)0202 electrical engineering electronic engineering information engineering010306 general physicsCluster analysiscomputer.programming_languageCentroidComputer Science ApplicationsHierarchical clustering010201 computation theory & mathematicsControl and Systems EngineeringAnti-Bayesian classification020201 artificial intelligence & image processingcomputerSoftwareQuantiloidsQuantile
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Discretized Bayesian Pursuit – A New Scheme for Reinforcement Learning

2012

Published version of a chapter in the book: Advanced Research in Applied Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-31087-4_79 The success of Learning Automata (LA)-based estimator algorithms over the classical, Linear Reward-Inaction ( L RI )-like schemes, can be explained by their ability to pursue the actions with the highest reward probability estimates. Without access to reward probability estimates, it makes sense for schemes like the L RI to first make large exploring steps, and then to gradually turn exploration into exploitation by making progressively smaller learning steps. However, this behavior becomes counter-intuitive wh…

Scheme (programming language)Mathematical optimizationDiscretizationLearning automataComputer sciencebusiness.industryVDP::Mathematics and natural science: 400::Information and communication science: 420::Algorithms and computability theory: 422estimator algorithmsBayesian probabilityBayesian reasoninglearning automataEstimatorVDP::Technology: 500::Information and communication technology: 550discretized learningBayesian inferenceAction (physics)Reinforcement learningArtificial intelligencepursuit schemesbusinesscomputercomputer.programming_language
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