000728347 000__ 03149cam\a2200397Ii\4500 000728347 001__ 728347 000728347 005__ 20230306141007.0 000728347 006__ m\\\\\o\\d\\\\\\\\ 000728347 007__ cr\cn\nnnunnun 000728347 008__ 150727s2015\\\\gw\a\\\\ob\\\\000\0\eng\d 000728347 020__ $$a9783658109936$$qelectronic book 000728347 020__ $$a3658109939$$qelectronic book 000728347 020__ $$z9783658109929 000728347 035__ $$aSP(OCoLC)ocn914472258 000728347 035__ $$aSP(OCoLC)914472258 000728347 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dGW5XE$$dN$T$$dIDEBK$$dAZU$$dYDXCP 000728347 049__ $$aISEA 000728347 050_4 $$aQA371 000728347 08204 $$a515/.357$$223 000728347 1001_ $$aSimon, Martin,$$eauthor. 000728347 24510 $$aAnomaly detection in random heterogeneous media$$h[electronic resource] :$$bFeynman-Kac formulae, stochastic homogenization and statistical inversion /$$cMartin Simon ; with a foreword by Prof. Dr. Lassi Päivärinta. 000728347 264_1 $$aWiesbaden :$$bSpringer Spektrum,$$c2015. 000728347 300__ $$a1 online resource (xiv, 150 pages) :$$billustrations 000728347 336__ $$atext$$btxt$$2rdacontent 000728347 337__ $$acomputer$$bc$$2rdamedia 000728347 338__ $$aonline resource$$bcr$$2rdacarrier 000728347 500__ $$a"Dissertation, Johannes Gutenberg University of Mainz, Germany, 2014." 000728347 504__ $$aIncludes bibliographical references. 000728347 506__ $$aAccess limited to authorized users. 000728347 520__ $$aThis monograph is concerned with the analysis and numerical solution of a stochastic inverse anomaly detection problem in electrical impedance tomography (EIT). Martin Simon studies the problem of detecting a parameterized anomaly in an isotropic, stationary and ergodic conductivity random field whose realizations are rapidly oscillating. For this purpose, he derives Feynman-Kac formulae to rigorously justify stochastic homogenization in the case of the underlying stochastic boundary value problem. The author combines techniques from the theory of partial differential equations and functional analysis with probabilistic ideas, paving the way to new mathematical theorems which may be fruitfully used in the treatment of the problem at hand. Moreover, the author proposes an efficient numerical method in the framework of Bayesian inversion for the practical solution of the stochastic inverse anomaly detection problem. Contents Feynman-Kac formulae Stochastic homogenization Statistical inverse problems Target Groups Students and researchers in the fields of inverse problems, partial differential equations, probability theory and stochastic processes Practitioners in the fields of tomographic imaging and noninvasive testing via EIT About the Author Martin Simon has worked as a researcher at the Institute of Mathematics at the University of Mainz from 2008 to 2014. During this period he had several research stays at the University of Helsinki. He has recently joined an asset management company as a financial mathematician. 000728347 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed July 29, 2015). 000728347 650_0 $$aInverse problems (Differential equations) 000728347 852__ $$bebk 000728347 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-658-10993-6$$zOnline Access$$91397441.1 000728347 909CO $$ooai:library.usi.edu:728347$$pGLOBAL_SET 000728347 980__ $$aEBOOK 000728347 980__ $$aBIB 000728347 982__ $$aEbook 000728347 983__ $$aOnline 000728347 994__ $$a92$$bISE