Description
Book SynopsisStatistical Computing in Nuclear Imaging introduces aspects of Bayesian computing in nuclear imaging. The book provides an introduction to Bayesian statistics and concepts and is highly focused on the computational aspects of Bayesian data analysis of photon-limited data acquired in tomographic measurements.
Basic statistical concepts, elements of decision theory, and counting statistics, including models of photon-limited data and Poisson approximations, are discussed in the first chapters. Monte Carlo methods and Markov chains in posterior analysis are discussed next 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 se
Table of Contents
Basic Statistical Concepts. Elements of Decision Theory. Counting Statistics. Monte Carlo Methods in Posterior Analysis. Basics of Nuclear Imaging. Statistical Computing. Appendix A Probability Distributions. Appendix B Elements of Set Theory. Appendix C Multinomial Distribution of Single-Voxel Imaging. Appendix D Derivations of Sampling Distribution Ratios. Appendix E Equation (6.11). Appendix F C++ Code of the OE Algorithm for STS. References.
