This coherent mathematical and statistical approach aimed at graduate students incorporates regression and topology as well as graph theory.
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Moo K. Chung is an Associate Professor in the Department of Biostatistics and Medical Informatics at the University of Wisconsin, Madison and is also affiliated with the Department of Statistics and Waisman Laboratory for Brain Imaging and Behavior. He has received the Vilas Associate Award for his research in applied topology to medical imaging, the Editor's Award for best paper published in the Journal of Speech, Language, and Hearing Research for a paper that analyzed CT images, and a National Institutes of Health (NIH) Brain Initiative Award for work on persistent homological brain network analysis. He has written numerous papers in computational neuroimaging and two previous books on computation on brain image analysis.
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Zustand: New. This tutorial reference provides a coherent overview of statistical and mathematical approaches used in brain network analysis. It goes beyond graph theory to explore different models in network science, regression, and algebraic topology, empowering method. Artikel-Nr. 271812202
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