Statistical Computing in Nuclear Imaging introduces Bayesian computing in nuclear imaging, covering basic statistical concepts, decision theory, counting statistics, Monte Carlo methods, Markov chains, and applications such as PET and SPECT. It includes illustrative examples of statistical computing, C++ code, and appendices covering probability distributions, set theory, multinomial distribution, and derivations of sampling distribution ratios. It is a valuable resource for physicists, mathematicians, engineers, and computer scientists, as well as practitioners of nuclear imaging data analysis. Format: Paperback / softback Length: 275 pages Publication date: 31 March 2021 Publisher: Taylor & Francis Ltd Statistical Computing in Nuclear Imaging introduces aspects of Bayesian computing in nuclear imaging, providing an introduction to Bayesian statistics and concepts and focusing on the computational aspects of Bayesian data analysis of photon-limited data acquired in tomographic measurements. The book covers basic statistical concepts, elements of decision theory, and counting statistics, including models of photon-limited data and Poisson approximations. Monte Carlo methods and Markov chains in posterior analysis are discussed, along with an introduction to nuclear imaging and applications such as PET and SPECT. The final chapter includes illustrative examples of statistical computing, based on Poisson-multinomial statistics. Examples include calculation of Bayes factors and risks as well as Bayesian decision making and hypothesis testing. Appendices cover probability distributions, elements of set theory, multinomial distribution of single-voxel imaging, and derivations of sampling distribution ratios. C++ code used in the final chapter is also provided. The text can be used as a textbook for physicists, mathematicians, engineers, and computer scientists, as well as a valuable resource for a wide spectrum of practitioners of nuclear imaging data analysis, including seasoned scientists and researchers who have not been exposed to Bayesian paradigms. Weight: 508g Dimension: 234 x 156 (mm) ISBN-13: 9780367783631
Related