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Monte Carlo simulation for the pharm...

Chang, Mark.

Monte Carlo simulation for the pharmaceutical industryconcepts, algorithms, and case studies /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Monte Carlo simulation for the pharmaceutical industryMark Chang.
Reminder of title:	concepts, algorithms, and case studies /
Author:	Chang, Mark.
Published:	Boca Raton :CRC Press,c2010.
Description:	1 online resource (539 p.)
Subject:	Drug Industrymethods.
Online resource:	http://www.crcnetbase.com/isbn/978-1-4398-3592-0
ISBN:	9781439835937 (electronic bk.)

Monte Carlo simulation for the pharmaceutical industryconcepts, algorithms, and case studies /
Chang, Mark.

Monte Carlo simulation for the pharmaceutical industryconcepts, algorithms, and case studies /[electronic resource] :Mark Chang. - Boca Raton :CRC Press,c2010. - 1 online resource (539 p.) - Chapman & Hall/CRC biostatistics series ;36. - Chapman & Hall/CRC biostatistics series ;38..

Includes bibliographical references and index.

"Preface Drug development, aiming at improving people's health, becomes more costly every year. The pharmaceutical industry must join its efforts with government and health professions to seek new, innovative, and cost- effective approaches in the development process. During this evolutionary process in the next decades, computer simulations will no doubt play a critical role. Computer simulation or Monte Carlo is the technique of simulating a dynamic system or process using a computer program. Computer simulations, as an efficient and effective research tool, have been used virtually in every concern of engineering, science, mathematics, etc. In this book, I am going to present the concept, theory, algorithm, and cases studies of Monte Carlo simulation in the pharmaceutical and health industries. The concepts refer not only to simulation in general, but also to various types of simulations in drug development. The theory will include virtual data sampling, game theory, deterministic and stochastic decision theories, adaptive design methods, Petrinet, genetic programming, resampling methods, and other strategies. These theories and methods either are necessary to carry out the simulations or make the simulations more efficient, even though there are many practical problems that can be simulated directly in ad hoc fashion without any theory of their efficiency or convergence considerations. The algorithms, which can be descriptive, computer pseudocode, or a combination of both, provide the basis for implementation of simulation methods. The case studies or applications are the simplified versions of the real world problems. These simplifications are necessary because a single case could otherwise occupy the whole book, preventing readers from exploring broad issues"--Provided by publisher.

ISBN: 9781439835937 (electronic bk.)Subjects--Topical Terms:

265448
Drug Industry
--methods.Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: RM301.25 / .C46 2010eb

Dewey Class. No.: 615/.190113

Monte Carlo simulation for the pharmaceutical industryconcepts, algorithms, and case studies /
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520 $a "Preface Drug development, aiming at improving people's health, becomes more costly every year. The pharmaceutical industry must join its efforts with government and health professions to seek new, innovative, and cost- effective approaches in the development process. During this evolutionary process in the next decades, computer simulations will no doubt play a critical role. Computer simulation or Monte Carlo is the technique of simulating a dynamic system or process using a computer program. Computer simulations, as an efficient and effective research tool, have been used virtually in every concern of engineering, science, mathematics, etc. In this book, I am going to present the concept, theory, algorithm, and cases studies of Monte Carlo simulation in the pharmaceutical and health industries. The concepts refer not only to simulation in general, but also to various types of simulations in drug development. The theory will include virtual data sampling, game theory, deterministic and stochastic decision theories, adaptive design methods, Petrinet, genetic programming, resampling methods, and other strategies. These theories and methods either are necessary to carry out the simulations or make the simulations more efficient, even though there are many practical problems that can be simulated directly in ad hoc fashion without any theory of their efficiency or convergence considerations. The algorithms, which can be descriptive, computer pseudocode, or a combination of both, provide the basis for implementation of simulation methods. The case studies or applications are the simplified versions of the real world problems. These simplifications are necessary because a single case could otherwise occupy the whole book, preventing readers from exploring broad issues"--Provided by publisher.
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