01002nam a22002531i 450099100267307970753620040225135640.0040624s1975 uika||||||||||||||||eng 0701120606b12960032-39ule_instARCHE-091771ExLDip.to Beni CulturaliitaA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l.Aristoteles4207Aristotle and Xenophon on Democracy and Oligarchy /translation with introductions and commentary by J. M. MooreLondon :Chatto & Windus,1975320 p. :ill. ;23 cmXenophonMoore, John Michael.b1296003202-04-1412-07-04991002673079707536LE001 AN IX 712001000053773le001C. 1-E0.00-l- 00000.i1356132712-07-04Aristotle and Xenophon on Democracy and Oligarchy280158UNISALENTOle00112-07-04ma -enguik0104435nam 2200973z- 450 991055715400332120210501(CKB)5400000000040517(oapen)https://directory.doabooks.org/handle/20.500.12854/68374(oapen)doab68374(EXLCZ)99540000000004051720202105d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierStochastic Models for Geodesy and Geoinformation ScienceBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (200 p.)3-03943-981-2 3-03943-982-0 In geodesy and geoinformation science, as well as in many other technical disciplines, it is often not possible to directly determine the desired target quantities. Therefore, the unknown parameters must be linked with the measured values by a mathematical model which consists of the functional and the stochastic models. The functional model describes the geometrical-physical relationship between the measurements and the unknown parameters. This relationship is sufficiently well known for most applications. With regard to the stochastic model, two problem domains of fundamental importance arise: 1. How can stochastic models be set up as realistically as possible for the various geodetic observation methods and sensor systems? 2. How can the stochastic information be adequately considered in appropriate least squares adjustment models? Further questions include the interpretation of the stochastic properties of the computed target values with regard to precision and reliability and the use of the results for the detection of outliers in the input data (measurements). In this Special Issue, current research results on these general questions are presented in ten peer-reviewed articles. The basic findings can be applied to all technical scientific fields where measurements are used for the determination of parameters to describe geometric or physical phenomena.History of engineering and technologybicssc3D straight line fittingARMA-processautoregressive processesB-spline approximationcollocation vs. adjustmentcolored noiseCONT14continuous processcovariance functiondata snoopingdirect solutionelementary error modelEM-algorithmErrors-In-Variables Modelextended Kalman filterfractional Gaussian noisegeneralized Hurst estimatorgeodetic network adjustmentGNSS phase biasGUM analysisHurst exponentinternal reliabilitylaser scanning datalikelihood ratio testmachine learningmean shift modelMonte Carlo integrationMonte Carlo simulationmulti-GNSSnonlinear least squares adjustmentobservation covariance matrixoutlierdetectionPPPprior informationprocess noiserandom number generatorrobustnesssensitivitysequential quasi-Monte Carlosingular dispersion matrixstochastic modelstochastic modelingstochastic propertiesterrestrial laser scannerterrestrial laser scanningtime seriestotal least squares (TLS)Total Least-Squaresvariance inflation modelvariance reductionvariance-covariance matrixvery long baseline interferometryweighted total least squares (WTLS)History of engineering and technologyNeitzel Frankedt1303359Neitzel FrankothBOOK9910557154003321Stochastic Models for Geodesy and Geoinformation Science3026943UNINA