Using state-of-the-art MRI images, this book illustrates radiological findings in the abdomen and pelvis in a case presentation format. Cases presented in this book include common and uncommon diseases of nearly every organ system of the abdomen and pelvis. Each case succinctly discusses the relevant imaging findings, differential diagnosis, and potential imaging and diagnostic pitfalls. Many cases also include discussion of MRI technique, with illustration of some common artifacts. For radiology residents and fellows, this book will be a valuable study tool and reference; fourth-year residents should find this book especially helpful when studying for oral boards. Practicing radiologists should find this a useful quick review of state-of-the-art body MRI.
The environmental sciences are undergoing a revolution in the use of models and data. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful new statistical tools for making sense of ecological processes. In Models for Ecological Data, James Clark introduces ecologists to these modern methods in modeling and computation. Assuming only basic courses in calculus and statistics, the text introduces readers to basic maximum likelihood and then works up to more advanced topics in Bayesian modeling and computation. Clark covers both classical statistical approaches and powerful new computational tools and describes how complexity can motivate a shift from classical to Bayesian methods. Through an available lab manual, the book introduces readers to the practical work of data modeling and computation in the language R. Based on a successful course at Duke University and National Science Foundation-funded institutes on hierarchical modeling, Models for Ecological Data will enable ecologists and other environmental scientists to develop useful models that make sense of ecological data. Consistent treatment from classical to modern Bayes Underlying distribution theory to algorithm development Many examples and applications Does not assume statistical background Extensive supporting appendixes Lab manual in R is available separately
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