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Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high;
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Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high;
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This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application;
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The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability;
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mining. Bayesian methods play a role central to the future of data and knowledge integration in the field of Bioinformatics. This book is devoted;
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> The inclusion of R & SAS code as well as the development of advanced methodology such as Bayesian and Markov models provides students with the;
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A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible;
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This accessible reference includes selected contributions from Bayesian Thinking - Modeling and Computation, Volume 25 in the Handbook;
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of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research, as well as;
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of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research, as well as;
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various fields such as stochastic processes and applications, data analysis methods and techniques, Bayesian methods, biostatistics, econometrics;
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Focusing on data commonly found in public health databases and clinical settings, Bayesian Disease Mapping: Hierarchical Modeling;
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Bayesian Methods in Finance provides a detailed overview of the theory of Bayesian methods and explains their real-world applications to;
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problems, and the Bayesian paradigm has a major role to play in the understanding of the complex interplay of context and individual predisposition;
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For the development of clinical decision support systems based on Bayesian networks, Mario A. Cypko investigates comprehensive expert;
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Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian;
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Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian;
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statistical methods for - timating model parameters and evaluating model t. However, the Bayesian methodology has shown great potential, particularly;
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of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has;
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Data Mining for Bioinformatics Applications provides valuable information on the data mining met;...
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Flexible Bayesian Regression Modeling is a step-by-step guide to the Bayesian revolution in regression modeling, for use in advanced;
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semi-parametric, frequentist and Bayesian encompassing both theoretical advances and emerging applications in a variety of scientific;
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Along with many practical applications, Bayesian Model Selection and Statistical Modeling presents an array of Bayesian inference and model;
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Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows;
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Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the;
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Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the;
Vergelijkbare producten zoals Bayesian Disease Mapping
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