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Advanced Methods of Pharmacokinetic and Pharmocodynamic Systems Analysis Volume 3 is vital to professionals and academicians working in drug development and bioengineering. Both basic and clinical scientists will benefit from this work. This book contains chapters by leading researchers in pharmacokinetic/pharmacodynamic modeling and will be of interest to anyone involved with the application of pharmacokinetic and pharmacodynamics to drug development. The use of mathematical modeling and associated computational methods is central to the study of the absorption, distribution and elimination of therapeutic drugs (pharmacokinetics) and to understanding how drugs produce their effects (pharmacodynamics). From its inception, the field of pharmacokinetics and pharmacodynamics has incorporated methods of mathematical modeling, simulation and computation in an effort to better understand and quantify the processes of uptake, disposition and action of therapeutic drugs. These methods for pharmacokinetic/pharmacodynamic systems analysis impact all aspects of drug development. In vitro, animal and human testing, as well as drug therapy are all influenced by these methods. Modeling methodologies developed for studying pharmacokinetic/ pharmacodynamic processes confront many challenges. This is related in part to the severe restrictions on the number and type of measurements that are available from laboratory experiments and clinical trials, as well as the variability in the experiments and the uncertainty associated with the processes themselves. The contributions are organized in three main areas: Mechanism-Based PK/PD, Pharmacometrics and Pharmacotherapy. Both professionals and academics will profit from this extensive work.

Pharmacokinetics. --- Pharmacology. --- Toxicology. --- Statistics. --- Computer industry. --- Biomedical engineering. --- Pharmacology/Toxicology. --- Statistics for Life Sciences, Medicine, Health Sciences. --- The Computing Profession. --- Biomedical Engineering and Bioengineering. --- Statistics . --- Computers. --- Clinical engineering --- Medical engineering --- Bioengineering --- Biophysics --- Engineering --- Medicine --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic brains --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Machine theory --- Calculators --- Cyberspace --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Drug effects --- Medical pharmacology --- Medical sciences --- Chemicals --- Chemotherapy --- Drugs --- Pharmacy --- Physiological effect --- Drug kinetics --- Kinetics, Drugs --- Chemical kinetics --- Pharmacology --- Kinetics --- Pharmacokinetics --- Metabolism

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Over the last ten years the introduction of computer intensive statistical methods has opened new horizons concerning the probability models that can be fitted to genetic data, the scale of the problems that can be tackled and the nature of the questions that can be posed. In particular, the application of Bayesian and likelihood methods to statistical genetics has been facilitated enormously by these methods. Techniques generally referred to as Markov chain Monte Carlo (MCMC) have played a major role in this process, stimulating synergies among scientists in different fields, such as mathematicians, probabilists, statisticians, computer scientists and statistical geneticists. Specifically, the MCMC "revolution" has made a deep impact in quantitative genetics. This can be seen, for example, in the vast number of papers dealing with complex hierarchical models and models for detection of genes affecting quantitative or meristic traits in plants, animals and humans that have been published recently. This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Most students in biology and agriculture lack the formal background needed to learn these modern biometrical techniques. Although a number of excellent texts in these areas have become available in recent years, the basic ideas and tools are typically described in a technically demanding style, and have been written by and addressed to professional statisticians. For this reason, considerable more detail is offered than what may be warranted for a more mathematically apt audience. The book is divided into four parts. Part I gives a review of probability and distribution theory. Parts II and III present methods of inference and MCMC methods. Part IV discusses several models that can be applied in quantitative genetics, primarily from a bayesian perspective. An effort has been made to relate biological to statistical parameters throughout, and examples are used profusely to motivate the developments.

519.226 --- 57.087.1 --- 575 --- Genetics --- -Monte Carlo method --- Markov processes --- Bayesian statistical decision theory. --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Stochastic processes --- Artificial sampling --- Model sampling --- Monte Carlo simulation --- Monte Carlo simulation method --- Stochastic sampling --- Games of chance (Mathematics) --- Mathematical models --- Numerical analysis --- Numerical calculations --- Biology --- Embryology --- Mendel's law --- Adaptation (Biology) --- Breeding --- Chromosomes --- Heredity --- Mutation (Biology) --- Variation (Biology) --- 575 General genetics. General cytogenetics. Immunogenetics. Evolution. Speciation. Phylogeny --- General genetics. General cytogenetics. Immunogenetics. Evolution. Speciation. Phylogeny --- 57.087.1 Biometry. Statistical study and treatment of biological data --- Biometry. Statistical study and treatment of biological data --- 519.226 Inference and decision theory. Likelihood. Bayesian theory. Fiducial probability --- Inference and decision theory. Likelihood. Bayesian theory. Fiducial probability --- Statistical methods --- Génétique quantitative --- Genetics -- Statistical methods. --- Monte Carlo Method --- Genetics, Medical --- Systems Analysis --- Basic Sciences. Genetics --- Population and Quantitative Genetics --- Population and Quantitative Genetics. --- Génétique --- Statistique bayésienne --- Life sciences. --- Biochemistry. --- Plant genetics. --- Animal genetics. --- Statistics. --- Life Sciences. --- Biochemistry, general. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Animal Genetics and Genomics. --- Plant Genetics & Genomics. --- Bayesian statistical decision theory --- Monte Carlo method --- Biomathematics. Biometry. Biostatistics --- Mathematical statistics --- Monte Carlo method. --- Markov processes. --- Statistical methods. --- Quantitative genetics --- Monte-Carlo, Méthode de --- Markov, Processus de --- Méthodes statistiques --- EPUB-LIV-FT SPRINGER-B --- Plant Genetics and Genomics. --- Statistics . --- Plants --- Statistical analysis --- Statistical data --- Statistical science --- Mathematics --- Econometrics --- Biological chemistry --- Chemical composition of organisms --- Organisms --- Physiological chemistry --- Chemistry --- Medical sciences --- Composition --- Markov --- Méthode de Monte Carlo --- Inférence --- GENETICS --- STATISTICS --- MONTE CARLO METHOD --- MARKOV CHAINS --- STATISTICS AND NUMERICAL DATA