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Stochastic geometry involves the study of random geometric structures, and blends geometric, probabilistic, and statistical methods to provide powerful techniques for modeling and analysis. Recent developments in computational statistical analysis, particularly Markov chain Monte Carlo, have enormously extended the range of feasible applications. Stochastic Geometry: Likelihood and Computation provides a coordinated collection of chapters on important aspects of the rapidly developing field of stochastic geometry, including: o a "crash-course" introduction to key stochastic geometry themes o considerations of geometric sampling bias issues o tesselations o shape o random sets o image analysis o spectacular advances in likelihood-based inference now available to stochastic geometry through the techniques of Markov chain Monte Carlo

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This book examines the theory of optimal model-based design and provides examples of optimal designs for various models, mostly related to biopharmaceutical applications, such as dose-response studies. The authors pay special attention to adaptive or sequential optimal designs for nonlinear regression models when estimation and optimal design are performed in stages. Ideal for researchers in regression analysis and experimental design, the text illustrates optimal designs for various models using an example of the application of a first-order optimization algorithm in the space of information matrices, which is implemented in both MATLAB and SAS.

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Through revealing photographs and accompanying text, this book offers an enchanting and beautiful glimpse into the inner life of the Institut des Hautes tudes Scientifiques (IHES). The IHES in France is an institute of advanced research in mathematics and theoretical physics with an interest in epistemology and the history of science. It provides exceptionally gifted scientists with a place where they can devote themselves entirely to their research, free of teaching and administrative constraints, and offers them the opportunity to invite visitors with whom they wish to work.

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