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Statistical Computing in Nuclear Imaging

eBook details

  • Author: Arkadiusz Sitek
  • File Size: 2 MB
  • Format: PDF
  • Length: 275 pages
  • Series: Series in Medical Physics and Biomedical Engineering Book 32
  • Publisher: CRC Press
  • Publication Date: December 17, 2014
  • Language: English
  • ASIN: B07L6V5W1F, B00S9OM0XY
  • ISBN-10: 143984934X
  • ISBN-13: 9781439849347
SKU: statistical-computing-in-nuclear-imaging-ebook Categories: , , , , , , Tags: , ,

$57.95 $8.00

Description

The ebook Statistical Computing in Nuclear Imaging (PDF) presents elements of Bayesian computing in nuclear imaging. The textbook supplies an intro to Bayesian stats and ideas and is extremely concentrated on the computational elements of Bayesian information analysis of photon-minimal information gotten in tomographic measurements. Basic statistical ideas, aspects of choice theory, and counting stats, consisting of designs of photon-minimal information and Poisson approximations, are gone over in the very first chapters. Monte Carlo approaches and Markov chains in the posterior analysis are gone over next in addition to an intro to nuclear imaging and applications such as SPECT and PET.

The last chapter consists of illustrative examples of statistical computing, based upon Poisson-multinomial stats. Examples consist of estimation of Bayes elements and dangers along with Bayesian choice making and hypothesis screening. Appendices cover possibility circulations, aspects of set theory, multinomial circulation of single-voxel imaging, and derivations of tasting circulation ratios. C++ code utilized in the last chapter is likewise offered.

The ebook can be utilized as a textbook that supplies an intro to Bayesian stats and advanced computing in medical imaging for mathematicians, physicists, engineers, and computer system researchers. It is likewise an important resource for a broad spectrum of professionals of nuclear imaging information analysis, consisting of skilled researchers and scientists who have actually not been exposed to Bayesian paradigms.

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