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A concise guide to mathematical modeling and analysis of pharmacokinetic data, this book contains valuable methods for maximizing the;
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to analyze data and to build models simulating biological phenomena. A summary chapter provides a view of trends in pharmacokinetic modeling;
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pharmacokinetics and its related fields, the book examines: Mathematical operations commonly used in pharmacokinetics Drug distribution and;
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Physiologically Based Pharmacokinetic (PBPK) Modeling: Methods and Applications in Toxicology and Risk Assessment presents foundational;
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basis of pharmacokinetic-pharmacodynamic modeling concepts it has become possible to describe and predict the time course of antimicrobial;
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for pharmacokinetic / pharmacodynamic analysis. Introduces requisite background to using Nonlinear Mixed Effects Modeling (NONMEM), covering;
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mathematical and computational approaches to biological problemsCovers all aspects of drug sensitivity modeling, starting from initial data;
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Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulations The first book dedicated to the emerging field of physiologically;
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of computational toxicology and its current and potential applications, the volume continues with 'best practices' in mathematical and computational;
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Measurement Data Modeling and Parameter Estimation integrates mathematical theory with engineering practice in the field of measurement;
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Measurement Data Modeling and Parameter Estimation integrates mathematical theory with engineering practice in the field of measurement;
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the tools of probability theory, difference and differential equations, numerical simulation, data analysis, and mathematical analysis;
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optimization of drug leads, and an exhaustive collection of pharmacokinetic data, arranged according to the structure of the drug, not its target or;
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The book comprises contributions by some of the most respected scientists in the field of mathematical modeling and numerical simulation;
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Presenting mathematical prerequisites in summary tables, this book explains fundamental techniques of mathematical modeling processes;
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increasingly turned to mathematical modeling to develop predictive systems. Kernel regression is a popular data modeling technique that can yield;
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Illustrates model interpolation and extrapolation to fill out missing or indeterminate data Covers both stochastic and deterministic mathematical;
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their data analysis toolbox. Mathematical Modeling of Social Relationships professes how mathematical modeling can help us understand the;
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This book provides a comprehensive survey of the pharmacokinetic models used for the quantitative interpretation of contrast-enhanced;
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mathematical modeling rather than the development of algebraic skills that might be needed in calculus. Functions, Data, and Models presents college;
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Fundamental techniques of mathematical modeling of processes essential to the food industry are explained in this text. Instead;
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This textbook is rich with real-life data sets, uses RStudio to streamline computations, builds big picture conceptual understandings;
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The book comprises contributions by some of the most respected scientists in the field of mathematical modeling and numerical simulation;
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in a particular subject area--is to provide an understanding of each method with a minimum of mathematical derivations. The chapters are;
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principles behind data networks. *then develops an understanding of the modeling issues and mathematical analysis needed to compare the effectiveness;
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select models, and figure out what data needs to be collected. By involving you in the mathematical process as early as possible -- beginning;
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and model development in the context of real data. The supporting mathematical, coding and biological background permit readers to explore a;
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