Buch online Statistical and Computational Inverse Problems (Applied Mathematical Sciences (160), Band 160)
Beschreibung Statistical and Computational Inverse Problems (Applied Mathematical Sciences (160), Band 160)
This book covers the statistical mechanics approach to computational solution of inverse problems, an innovative area of current research with very promising numerical results. The techniques are applied to a number of real world applications such as limited angle tomography, image deblurring, electical impedance tomography, and biomagnetic inverse problems. Contains detailed examples throughout and includes a chapter on case studies where such methods have been implemented in biomedical engineering.
Statistical and Computational Inverse Problems (Applied Mathematical Sciences (160), Band 160) PDF ePub
Statistical and Computational Inverse Problems Applied ~ Statistical and Computational Inverse Problems (Applied Mathematical Sciences (160), Band 160) / Kaipio, Jari, Somersalo, E. / ISBN: 9780387220734 / Kostenloser Versand für alle Bücher mit Versand und Verkauf duch .
Statistical and Computational Inverse Problems / SpringerLink ~ The book develops the statistical approach to inverse problems with an emphasis on modeling and computations. The framework is the Bayesian paradigm, where all variables are modeled as random variables, the randomness reflecting the degree of belief of their values, and the solution of the inverse problem is expressed in terms of probability densities. The book discusses in detail the construction of prior models, the measurement noise modeling and Bayesian estimation. Markov Chain Monte .
Statistical and Computational Inverse Problems Applied ~ Statistical and Computational Inverse Problems (Applied Mathematical Sciences, Band 160) / Jari Kaipio, E. Somersalo / ISBN: 9781441919649 / Kostenloser Versand für alle Bücher mit Versand und Verkauf duch .
Statistical and Computational Inverse Problems (Applied ~ The book develops the statistical approach to inverse problems with an emphasis on modeling and computations. The framework is the Bayesian paradigm, where all variables are modeled as random variables, the randomness reflecting the degree of belief of their values, and the solution of the inverse problem is expressed in terms of probability densities. The book discusses in detail the construction of prior models, the measurement noise modeling and Bayesian estimation. Markov Chain Monte .
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