Literatura científica selecionada sobre o tema "Geometrisk statistik"
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Artigos de revistas sobre o assunto "Geometrisk statistik"
Refiadi, Gunawan. "PEMBUATAN KOMPONEN AEROSPACE Al 6082-T6511 DENGAN METODE ONE STOP MACHINING MENGGUNAKAN MESIN CNC MULTITASKING". Jurnal Teknologi Bahan dan Barang Teknik 4, n.º 2 (31 de dezembro de 2014): 55. http://dx.doi.org/10.37209/jtbbt.v4i2.52.
Texto completo da fonteBugatekin, Ayşe. "GEOMETRİK DAĞILIMDAKİ SIRA İSTATİSTİKLERİN ÖRNEK MİNİMUMUNUN MOMENT ÇIKARAN FONKSİYONU". e-Journal of New World Sciences Academy 10, n.º 3 (13 de julho de 2015): 51–54. http://dx.doi.org/10.12739/nwsa.2015.10.3.3a0073.
Texto completo da fonteMehta, Anita, G. C. Barker e J. M. Luck. "Cooperativity in sandpiles: statistics of bridge geometries". Journal of Statistical Mechanics: Theory and Experiment 2004, n.º 10 (29 de outubro de 2004): P10014. http://dx.doi.org/10.1088/1742-5468/2004/10/p10014.
Texto completo da fonteSuwito, Tukimun Darmo, Yuswal Subby e Fajrina Adhe Yossa. "PEMODELAN DAERAH RAWAN KECELAKAAN PADA RUAS JALAN MT HARYONO DI KOTA SAMARINDA". SENTRI: Jurnal Riset Ilmiah 2, n.º 3 (5 de março de 2023): 607–17. http://dx.doi.org/10.55681/sentri.v2i3.472.
Texto completo da fonteManagerxot, Jek, Roy Nusa e Nunik Kusumawardani. "METODE ALTERNATIF HITUNG IPKM YANG MEMILIKI KORELASI LEBIH TINGGI DENGAN IPM". JURNAL EKOLOGI KESEHATAN 16, n.º 2 (29 de agosto de 2018): 112–20. http://dx.doi.org/10.22435/jek.16.2.363.112-120.
Texto completo da fonteBombelli, L., A. Corichi e O. Winkler. "Semiclassical quantum gravity: statistics of combinatorial Riemannian geometries". Annalen der Physik 517, n.º 8 (12 de julho de 2005): 499–519. http://dx.doi.org/10.1002/andp.20055170803.
Texto completo da fonteWang, Libing. "Modeling complex reservoir geometries with multiple-point statistics". Mathematical Geology 28, n.º 7 (outubro de 1996): 895–907. http://dx.doi.org/10.1007/bf02066007.
Texto completo da fonteBombelli, L., A. Corichi e O. Winkler. "Semiclassical quantum gravity: statistics of combinatorial Riemannian geometries". Annalen der Physik 14, n.º 8 (1 de agosto de 2005): 499–519. http://dx.doi.org/10.1002/andp.200410144.
Texto completo da fontePrasetyo, Yudo. "STATE-OF-ART KONSERVASI BANGUNAN DAN CAGAR BUDAYA MELALUI PEMBENTUKAN MODEL 3 DIMENSI BERBASIS TEKNIK FOTOGRAMMETRI RENTANG DEKAT". Elipsoida : Jurnal Geodesi dan Geomatika 1, n.º 02 (23 de novembro de 2018): 14–20. http://dx.doi.org/10.14710/elipsoida.2018.3698.
Texto completo da fonteUtomo, Puji Dwi, Thomas Sukardi e Sudji Munadi. "Analisis Kualitas Geometris Hasil Praktik Pemesinan Bubut Siswa SMK Jurusan Teknik Pemesinan". JURNAL DINAMIKA VOKASIONAL TEKNIK MESIN 2, n.º 1 (1 de abril de 2017): 1. http://dx.doi.org/10.21831/dinamika.v2i1.13509.
Texto completo da fonteTeses / dissertações sobre o assunto "Geometrisk statistik"
Pedersen, Morten Akhøj. "Méthodes riemanniennes et sous-riemanniennes pour la réduction de dimension". Electronic Thesis or Diss., Université Côte d'Azur, 2023. http://www.theses.fr/2023COAZ4087.
Texto completo da fonteIn this thesis, we propose new methods for dimension reduction based on differential geometry, that is, finding a representation of a set of observations in a space of lower dimension than the original data space. Methods for dimension reduction form a cornerstone of statistics, and thus have a very wide range of applications. For instance, a lower dimensional representation of a data set allows visualization and is often necessary for subsequent statistical analyses. In ordinary Euclidean statistics, the data belong to a vector space and the lower dimensional space might be a linear subspace or a non-linear submanifold approximating the observations. The study of such smooth manifolds, differential geometry, naturally plays an important role in this last case, or when the data space is itself a known manifold. Methods for analysing this type of data form the field of geometric statistics. In this setting, the approximating space found by dimension reduction is naturally a submanifold of the given manifold. The starting point of this thesis is geometric statistics for observations belonging to a known Riemannian manifold, but parts of our work form a contribution even in the case of data belonging to Euclidean space, mathbb{R}^d.An important example of manifold valued data is shapes, in our case discrete or continuous curves or surfaces. In evolutionary biology, researchers are interested in studying reasons for and implications of morphological differences between species. Shape is one way to formalize morphology. This application motivates the first main contribution of the thesis. We generalize a dimension reduction method used in evolutionary biology, phylogenetic principal component analysis (P-PCA), to work for data on a Riemannian manifold - so that it can be applied to shape data. P-PCA is a version of PCA for observations that are assumed to be leaf nodes of a phylogenetic tree. From a statistical point of view, the important property of such data is that the observations (leaf node values) are not necessarily independent. We define and estimate intrinsic weighted means and covariances on a manifold which takes the dependency of the observations into account. We then define phylogenetic PCA on a manifold to be the eigendecomposition of the weighted covariance in the tangent space of the weighted mean. We show that the mean estimator that is currently used in evolutionary biology for studying morphology corresponds to taking only a single step of our Riemannian gradient descent algorithm for the intrinsic mean, when the observations are represented in Kendall's shape space. Our second main contribution is a non-parametric method for dimension reduction that can be used for approximating a set of observations based on a very flexible class of submanifolds. This method is novel even in the case of Euclidean data. The method works by constructing a subbundle of the tangent bundle on the data manifold via local PCA. We call this subbundle the principal subbundle. We then observe that this subbundle induces a sub-Riemannian structure and we show that the resulting sub-Riemannian geodesics with respect to this structure stay close to the set of observations. Moreover, we show that sub-Riemannian geodesics starting from a given point locally generate a submanifold which is radially aligned with the estimated subbundle, even for non-integrable subbundles. Non-integrability is likely to occur when the subbundle is estimated from noisy data, and our method demonstrates that sub-Riemannian geometry is a natural framework for dealing which such problems. Numerical experiments illustrate the power of our framework by showing that we can achieve impressively large range reconstructions even in the presence of quite high levels of noise
I denne afhandling præsenteres nye metoder til dimensionsreduktion, baseret p˚adifferential geometri. Det vil sige metoder til at finde en repræsentation af et datasæti et rum af lavere dimension end det opringelige rum. S˚adanne metoder spiller enhelt central rolle i statistik, og har et meget bredt anvendelsesomr˚ade. En laveredimensionalrepræsentation af et datasæt tillader visualisering og er ofte nødvendigtfor efterfølgende statistisk analyse. I traditionel, Euklidisk statistik ligger observationernei et vektor rum, og det lavere-dimensionale rum kan være et lineært underrumeller en ikke-lineær undermangfoldighed som approksimerer observationerne.Studiet af s˚adanne glatte mangfoldigheder, differential geometri, spiller en vigtig rollei sidstnævnte tilfælde, eller hvis rummet hvori observationerne ligger i sig selv er enmangfoldighed. Metoder til at analysere observationer p˚a en mangfoldighed udgørfeltet geometrisk statistik. I denne kontekst er det approksimerende rum, fundetvia dimensionsreduktion, naturligt en submangfoldighed af den givne mangfoldighed.Udgangspunktet for denne afhandling er geometrisk statistik for observationer p˚a ena priori kendt Riemannsk mangfoldighed, men dele af vores arbejde udgør et bidragselv i tilfældet med observationer i Euklidisk rum, Rd.Et vigtigt eksempel p˚a data p˚a en mangfoldighed er former, i vores tilfældediskrete kurver eller overflader. I evolutionsbiologi er forskere interesseret i at studeregrunde til og implikationer af morfologiske forskelle mellem arter. Former er ´en m˚adeat formalisere morfologi p˚a. Denne anvendelse motiverer det første hovedbidrag idenne afhandling. We generaliserer en metode til dimensionsreduktion brugt i evolutionsbiologi,phylogenetisk principal component analysis (P-PCA), til at virke for datap˚a en Riemannsk mangfoldighed - s˚a den kan anvendes til observationer af former. PPCAer en version af PCA for observationer som antages at være de yderste knuder iet phylogenetisk træ. Fra et statistisk synspunkt er den vigtige egenskab ved s˚adanneobservationer at de ikke nødvendigvis er uafhængige. We definerer og estimerer intrinsiskevægtede middelværdier og kovarianser p˚a en mangfoldighed, som tager højde fors˚adanne observationers afhængighed. Vi definerer derefter phylogenetisk PCA p˚a enmangfoldighed som egendekomposition af den vægtede kovarians i tanget-rummet tilden vægtede middelværdi. Vi viser at estimatoren af middelværdien som pt. bruges ievolutionsbiologi til at studere morfologi svarer til at tage kun et enkelt skridt af voresRiemannske gradient descent algoritme for den intrinsiske middelværdi, n˚ar formernerepræsenteres i Kendall´s form-mangfoldighed.Vores andet hovedbidrag er en ikke-parametrisk metode til dimensionsreduktionsom kan bruges til at approksimere et data sæt baseret p˚a en meget flexibel klasse afsubmangfoldigheder. Denne metode er ny ogs˚a i tilfældet med Euklidisk data. Metodenvirker ved at konstruere et under-bundt af tangentbundet p˚a datamangfoldighedenM via lokale PCA´er. Vi kalder dette underbundt principal underbundtet. Viobserverer at dette underbundt inducerer en sub-Riemannsk struktur p˚a M og vi viserat sub-Riemannske geodæter fra et givent punkt lokalt genererer en submangfoldighedsom radialt flugter med det estimerede subbundt, selv for ikke-integrable subbundter.Ved støjfyldt data forekommer ikke-integrabilitet med stor sandsynlighed, og voresmetode demonstrerer at sub-Riemannsk geometri er en naturlig tilgang til at h˚andteredette. Numeriske eksperimenter illustrerer styrkerne ved metoden ved at vise at denopn˚ar rekonstruktioner over store afstande, selv under høje niveauer af støj
Saive, Yannick. "DirCNN: Rotation Invariant Geometric Deep Learning". Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252573.
Texto completo da fonteNyligen har ämnet geometrisk deep learning presenterat ett nytt sätt för maskininlärningsalgoritmer att arbeta med punktmolnsdata i dess råa form.Banbrytande arkitekturer som PointNet och många andra som byggt på dennes framgång framhåller vikten av invarians under inledande datatransformationer. Sådana transformationer inkluderar skiftning, skalning och rotation av punktmoln i ett tredimensionellt rum. Precis som vi önskar att klassifierande maskininlärningsalgoritmer lyckas identifiera en uppochnedvänd hund som en hund vill vi att våra geometriska deep learning-modeller framgångsrikt ska kunna hantera transformerade punktmoln. Därför använder många modeller en inledande datatransformation som tränas som en del av ett neuralt nätverk för att transformera punktmoln till ett globalt kanoniskt rum. Jag ser tillkortakommanden i detta tillgångavägssätt eftersom invariansen är inte fullständigt garanterad, den är snarare approximativ. För att motverka detta föreslår jag en lokal deterministisk transformation som inte måste läras från datan. Det nya lagret i det här projektet bygger på Edge Convolutions och döps därför till DirEdgeConv, namnet tar den riktningsmässiga invariansen i åtanke. Lagret ändras en aning för att introducera ett nytt lager vid namn DirSplineConv. Dessa lager sätts ihop i olika modeller som sedan jämförs med sina efterföljare på samma uppgifter för att ge en rättvis grund för att jämföra dem. Resultaten är inte lika bra som toppmoderna resultat men de är ändå tillfredsställande. Jag tror även resultaten kan förbättas genom att förbättra inlärningshastigheten och dess schemaläggning. I ett experiment där ablation genomförs på de nya lagren ser vi att lagrens huvudkoncept förbättrar resultaten överlag.
Granquist, Daniel. "Genom statistisk analys utvärdera geometriska parametrars påverkan på ett XPI- cylinderhuvuds snurrtal". Thesis, KTH, Maskinkonstruktion (Inst.), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-99453.
Texto completo da fonteDetta examensarbete är ett produktutvecklingsarbete som har genomförts i samarbete med Scania CV AB. Syftet med arbetet har varit att finna ett samband mellan XPI- cylinderhuvudets geometriska parametrar och dess snurrtal för att förbättra konstruktionsunderlaget för cylinderhuvudet. Samhällets ökade fokus kring miljöfrågorna för med sig allt hårdare emissionslagstiftningar för lastbilstillverkarna. För att driva utvecklingen framåt läggs stora resurser på forskning och utveckling av dieselmotorn och dess förbränningsförlopp. Insugningsluftens strömning in i cylindern har en stor påverkan vid bildandet av emissioner och den typ av strömningen som söks kallas för snurr. Snurr fås då luften strömmar runt cylinderns vertikala axel inne i förbränningsrummet. På tidigare cylinderhuvuden som Scania CV AB tagit fram har klara samband setts mellan snurren och geometriska parametrar hos cylinderhuvudet men med det nya XPI- cylinderhuvudet ses att även andra parametrar är med och påverkar snurren. Av erfarenhet är det känt att flertalet geometrier påverkar snurren och flödet som fås ner i cylindern. Utifrån dessa kunskaper och intervjuer med sakkunniga på Scania CV AB har de parametrar som ska ingå i studien tagits fram. Efter definiering av de geometriska parametrarna, 33 stycken, har 120 cylinderhuvuden mätts upp och snurrtestats. Den statistiska undersökningen har genomförts av Ekaterina Fetisova som en del av hennes kandidatexamensprojekt för institutionen Matematisk Statistik på Stockholms Universitet. Undersökningen ledde till att sex modeller togs fram, med hjälp av multipel linjär regression, som representerar de geometriska parametrarnas påverkan på snurrtalet. De framtagna modellerna förklarar mellan 54-60 % av variationen hos snurren och ett fåtal geometriska parametrar sticker ut som viktigare än andra, vissa mer förväntade än andra. Slutsatser som dras är att viktiga parametrar troligtvis saknas i undersökningen och att det finns en stor komplexitet mellan de geometriska parametrarna och snurrtalet. För att få en bättre förståelse för kanalernas individuella påverkan på snurren rekommenderas att varje kanal snurrtestas var och en för sig och kopplas mot de geometriska parametrarna för respektive kanal.
Ho, Pak-kei. "Parametric and non-parametric inference for Geometric Process". Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B31483859.
Texto completo da fonteHo, Pak-kei, e 何柏基. "Parametric and non-parametric inference for Geometric Process". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B31483859.
Texto completo da fonteKeil, Mitchel J. "Automatic generation of interference-free geometric models of spatial mechanisms". Diss., This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-08252008-162631/.
Texto completo da fonteSchäfer, Philip Morten [Verfasser]. "Statistics, Geometries and Scaling Laws of Streamlines and Streamline Segments in Turbulent Flows / Philip Morten Schäfer". Aachen : Shaker, 2013. http://d-nb.info/105157398X/34.
Texto completo da fonteSCHIAVON, JACOPO. "Geometria differenziale delle matrici simmetriche e definite positive per applicazioni statistiche". Doctoral thesis, Università degli studi di Padova, 2022. http://hdl.handle.net/11577/3449438.
Texto completo da fonteDifferential geometry is the set of tools that allows to perform the usual mathematical tasks of algebra and calculus on spaces that do not behave like Euclidean vector spaces, for instance points on a curved surface. This field of mathematics is becoming more and more relevant in multiple fields, statistics and machine learning among those, due to the enormous availability of data belonging to increasingly complex domains. An example among many of such complex domains is the set of Symmetric and Positive Definite matrices, i.e. the set of covariance matrices, that appears frequently in medical imaging but is also used often as parameter space in statistical modeling scenarios. The aim of this thesis is to collect and organize the scattered knowledge on the Riemannian geometry of the symmetric and positive definite matrices, and to build practical techniques using the tools of differential geometry that can be readily applied within a pipeline of statistical analysis. This has been achieved with two different methods: the first is a quasi-Newton algorithm for Riemannian optimization that can be plugged in any situation in which maximization of a function of symmetric and positive definite matrices is required, such as those that arise in the context of likelihood inference and variational approximation. The second is a Riemannian registration algorithm to perform a pre-processing of symmetric and positive definite data such as those arising from medical imaging or brain computer interface. This algorithm, among other properties, provides a theoretical framework to focus the analysis on the eigenvalues of the analyzed matrices, allowing the employment of Euclidean methods for statistical inference also in a Riemannian context.
Suttmiller, Alexander Gage. "Streamline Feature Detection: Geometric and Statistical Evaluation of Streamline Properties". The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1315967677.
Texto completo da fonteVilla, E. "Methods of geometric measure theory in stochastic geometry". Doctoral thesis, Università degli Studi di Milano, 2007. http://hdl.handle.net/2434/28369.
Texto completo da fonteLivros sobre o assunto "Geometrisk statistik"
Fang, Kʻai-tʻai. Number-theoretic methods in statistics. London: Chapman & Hall, 1994.
Encontre o texto completo da fontePawlowsky-Glahn, Vera. Modelling and analysis of compositional data. Chichester, West Sussex: John Wiley & Sons, Inc., 2015.
Encontre o texto completo da fonteRoux, Brigitte Le. Combinatorial Inference in Geometric Data Analysis. Boca Raton, Florida, USA: Chapman and Hall/CRC, Taylor & Francis Group, 2019.
Encontre o texto completo da fonteV, Buldygin V., e Kharazishvili A. B, eds. Geometric aspects of probability theory and mathematical statistics. Dordrecht: Kluwer Academic, 2000.
Encontre o texto completo da fonteStoyan, Dietrich. Fractals, random shapes, and point fields: Methods of geometrical statistics. Chichester: Wiley, 1994.
Encontre o texto completo da fonte1911-, Ledermann Walter, e Vajda Steven 1901-, eds. Handbook of applicable mathematics. Chichester: Wiley, 1985.
Encontre o texto completo da fonteStoyan, Dietrich. Stochastic geometry and its applications. Chichester [W. Sussex]: Wiley, 1987.
Encontre o texto completo da fonteStoyan, Dietrich. Stochastic geometry and its applications. 2a ed. Chichester: Wiley, 1995.
Encontre o texto completo da fonteMauldin, R. Daniel. Graph directed Markov systems: Geometry and dynamics of limit sets. Cambridge: Cambridge University Press, 2003.
Encontre o texto completo da fonteFlewelling, Gary. Math activities using LogoWriter: Probability and statistics. International Society for Technology in Education, 1994.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Geometrisk statistik"
Kühnel, Line, Tom Fletcher, Sarang Joshi e Stefan Sommer. "Latent Space Geometric Statistics". In Pattern Recognition. ICPR International Workshops and Challenges, 163–78. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68780-9_16.
Texto completo da fonteMarshall, Albert W., Ingram Olkin e Barry C. Arnold. "Geometric Inequalities". In Springer Series in Statistics, 269–96. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-0-387-68276-1_8.
Texto completo da fonteMecke, Joseph, Rolf G. Schneider, Dietrich Stoyan e Wolfgang R. R. Weil. "Statistik für einige Modelle der Stochastischen Geometrie". In DMV Seminar, 121–64. Basel: Birkhäuser Basel, 1990. http://dx.doi.org/10.1007/978-3-0348-7029-0_5.
Texto completo da fonteSaville, David J., e Graham R. Wood. "The Geometric Tool Kit". In Springer Texts in Statistics, 10–38. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-0971-3_2.
Texto completo da fonteKosambi, D. D. "The Geometric Method in Mathematical Statistics". In D.D. Kosambi, 131–39. New Delhi: Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-3676-4_17.
Texto completo da fonteChang, Ted. "Tangent Space Approximation in Geometric Statistics". In Springer Handbook of Engineering Statistics, 1059–73. London: Springer London, 2023. http://dx.doi.org/10.1007/978-1-4471-7503-2_53.
Texto completo da fonteLeung, Kit-Nam. "Arithmetic and Geometric Processes". In Springer Handbook of Engineering Statistics, 931–55. London: Springer London, 2006. http://dx.doi.org/10.1007/978-1-84628-288-1_49.
Texto completo da fonteHitzer, Eckhard, e Dietmar Hildenbrand. "Introduction to Geometric Algebra". In Springer Proceedings in Mathematics & Statistics, 1–41. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55985-3_1.
Texto completo da fonteScheaffer, Richard L., Ann Watkins, Mrudulla Gnanadesikan e Jeffrey A. Witmer. "Waiting for Reggie Jackson: The Geometric Distribution". In Activity-Based Statistics, 79–81. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-3843-8_17.
Texto completo da fonteBroniatowski, Michel, e Wolfgang Stummer. "Some Universal Insights on Divergences for Statistics, Machine Learning and Artificial Intelligence". In Geometric Structures of Information, 149–211. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02520-5_8.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Geometrisk statistik"
CAI, JUN, e JOSÉ GARRIDO. "ASYMPTOTIC FORMS AND BOUNDS FOR TAILS OF CONVOLUTIONS OF COMPOUND GEOMETRIC DISTRIBUTIONS, WITH APPLICATIONS". 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_0010.
Texto completo da fonteSudsuk, Areeya, e Winai Bodhisuwan. "The Topp-Leone geometric distribution". In 2016 12th International Conference on Mathematics, Statistics, and Their Application (ICMSA). IEEE, 2016. http://dx.doi.org/10.1109/icmsa.2016.7954319.
Texto completo da fonteKvinge, Henry, Elin Farnell, Jingya Li e Yujia Chen. "Rare Geometries: Revealing Rare Categories via Dimension-Driven Statistics". In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00052.
Texto completo da fonteTing, Dai. "Statistics Properties of Geometric Brown Motion under Haar Wavelet". In 2009 First International Conference on Information Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/icise.2009.1090.
Texto completo da fonteFletcher, P. Thomas, Suresh Venkatasubramanian e Sarang Joshi. "Robust statistics on Riemannian manifolds via the geometric median". In 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2008. http://dx.doi.org/10.1109/cvpr.2008.4587747.
Texto completo da fonteLi, Lee Siaw, e Maman A. Djauhari. "Monitoring autocorrelated process: A geometric Brownian motion process approach". In INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND STATISTICS 2013 (ICMSS2013): Proceedings of the International Conference on Mathematical Sciences and Statistics 2013. AIP, 2013. http://dx.doi.org/10.1063/1.4823976.
Texto completo da fonteSagadavan, Revathi, e Maman A. Djauhari. "Autocorrelated multivariate process control: A geometric Brownian motion approach". In INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND STATISTICS 2013 (ICMSS2013): Proceedings of the International Conference on Mathematical Sciences and Statistics 2013. AIP, 2013. http://dx.doi.org/10.1063/1.4823979.
Texto completo da fonteWaagen, Donald, Don Hulsey, Jamie Godwin e David Gray. "A geometric statistic for deep learning model confidence and adversarial defense". In Automatic Target Recognition XXXII, editado por Kristen Jaskie, Timothy L. Overman, Riad I. Hammoud e Abhijit Mahalanobis. SPIE, 2022. http://dx.doi.org/10.1117/12.2618299.
Texto completo da fonteLei, Li, e LongTing Wang. "Geometric and topological structures of complex numbers". In International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), editado por Ke Chen, Nan Lin, Romeo Meštrović, Teresa A. Oliveira, Fengjie Cen e Hong-Ming Yin. SPIE, 2022. http://dx.doi.org/10.1117/12.2628101.
Texto completo da fonteWemhoff, Aaron P., e Geoffrey Haas. "Molecular Dynamics Problem Initialization and Statistics Collection for Arbitrary Geometries". In ASME 2009 Heat Transfer Summer Conference collocated with the InterPACK09 and 3rd Energy Sustainability Conferences. ASMEDC, 2009. http://dx.doi.org/10.1115/ht2009-88072.
Texto completo da fonteRelatórios de organizações sobre o assunto "Geometrisk statistik"
Singer, D. A., e R. Kouda. Application of geometric probability and Bayesian statistics to the search for mineral deposits. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128119.
Texto completo da fonte