Stochastic Models In The Life Sciences And Their Methods Of Analysis
Author: Frederic Y. M. Wan
Publisher:
Published: 2019
Total Pages: 476
ISBN-13: 9789813274617
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Stochastic Models In The Life Sciences And Their Methods Of Analysis
Author: Wan Frederic Y M
Publisher: World Scientific
Published: 2019-08-29
Total Pages: 476
ISBN-13: 981327462X
DOWNLOAD EBOOK →Biological processes are evolutionary in nature and often evolve in a noisy environment or in the presence of uncertainty. Such evolving phenomena are necessarily modeled mathematically by stochastic differential/difference equations (SDE), which have been recognized as essential for a true understanding of many biological phenomena. Yet, there is a dearth of teaching material in this area for interested students and researchers, notwithstanding the addition of some recent texts on stochastic modelling in the life sciences. The reason may well be the demanding mathematical pre-requisites needed to 'solve' SDE.A principal goal of this volume is to provide a working knowledge of SDE based on the premise that familiarity with the basic elements of a stochastic calculus for random processes is unavoidable. Through some SDE models of familiar biological phenomena, we show how stochastic methods developed for other areas of science and engineering are also useful in the life sciences. In the process, the volume introduces to biologists a collection of analytical and computational methods for research and applications in this emerging area of life science. The additions broaden the available tools for SDE models for biologists that have been limited by and large to stochastic simulations.
An Introduction to Stochastic Modeling
Author: Howard M. Taylor
Publisher: Academic Press
Published: 2014-05-10
Total Pages: 410
ISBN-13: 1483269272
DOWNLOAD EBOOK →An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.
Stochastic Models in Biology
Author: Narendra S. Goel
Publisher: Elsevier
Published: 2013-10-22
Total Pages: 282
ISBN-13: 1483278107
DOWNLOAD EBOOK →Stochastic Models in Biology describes the usefulness of the theory of stochastic process in studying biological phenomena. The book describes analysis of biological systems and experiments though probabilistic models rather than deterministic methods. The text reviews the mathematical analyses for modeling different biological systems such as the random processes continuous in time and discrete in state space. The book also discusses population growth and extinction through Malthus' law and the work of MacArthur and Wilson. The text then explains the dynamics of a population of interacting species. The book also addresses population genetics under systematic evolutionary pressures known as deterministic equations and genetic changes in a finite population known as stochastic equations. The text then turns to stochastic modeling of biological systems at the molecular level, particularly the kinetics of biochemical reactions. The book also presents various useful equations such as the differential equation for generating functions for birth and death processes. The text can prove valuable for biochemists, cellular biologists, and researchers in the medical and chemical field who are tasked to perform data analysis.
Mathematical Modeling for the Life Sciences
Author: Jacques Istas
Publisher: Springer Science & Business Media
Published: 2005-10-04
Total Pages: 170
ISBN-13: 354027877X
DOWNLOAD EBOOK →Provides a wide range of mathematical models currently used in the life sciences Each model is thoroughly explained and illustrated by example Includes three appendices to allow for independent reading
Stochastic Modeling in Physical and Biological Sciences
Author: V. Thangaraj
Publisher:
Published: 2016-06-28
Total Pages: 256
ISBN-13: 9788184875447
DOWNLOAD EBOOK →Discusses basic definitions, important properties and results on Markov Chains giving examples to understand the intricacies of the theory of Markov Chains. This book elaborates continuous time stochastic processes for modeling purpose explaining in detail with examples and includes an application oriented chapter on how stochastic modeling throws light on physical sciences. Basics on branching processes and their applications are explained pedagogically with a view to develop modeling capacity in biological sciences. Queues are a ubiquitous part of everyday life. This volume throws light on the foundation for stochastic modeling on queues. Interestingly, it presents bulk service queues an offshoot of professor Medhi's foundation on queues. A monumental and seminal contribution of Professor Neuts on matrix geometric methods is presented in a neat form. Finally, it meticulously designs some stochastic biological models and shows how stochastic modeling can project the prosperity of biological sciences.
Introduction to Modeling and Analysis of Stochastic Systems
Author: V. G. Kulkarni
Publisher: Springer
Published: 2010-11-03
Total Pages: 313
ISBN-13: 1441917721
DOWNLOAD EBOOK →This book provides a self-contained review of all the relevant topics in probability theory. A software package called MAXIM, which runs on MATLAB, is made available for downloading. Vidyadhar G. Kulkarni is Professor of Operations Research at the University of North Carolina at Chapel Hill.
Methods and Models in Mathematical Biology
Author: Johannes Müller
Publisher: Springer
Published: 2015-08-13
Total Pages: 711
ISBN-13: 3642272517
DOWNLOAD EBOOK →This book developed from classes in mathematical biology taught by the authors over several years at the Technische Universität München. The main themes are modeling principles, mathematical principles for the analysis of these models and model-based analysis of data. The key topics of modern biomathematics are covered: ecology, epidemiology, biochemistry, regulatory networks, neuronal networks and population genetics. A variety of mathematical methods are introduced, ranging from ordinary and partial differential equations to stochastic graph theory and branching processes. A special emphasis is placed on the interplay between stochastic and deterministic models.
Stochastic Processes and Applications in Biology and Medicine
Author: Marius Iosifescu
Publisher:
Published: 1973
Total Pages: 348
ISBN-13:
DOWNLOAD EBOOK →Vol. 2.
Deterministic Versus Stochastic Modelling in Biochemistry and Systems Biology
Author: Paola Lecca
Publisher: Elsevier
Published: 2013-04-09
Total Pages: 411
ISBN-13: 1908818212
DOWNLOAD EBOOK →Stochastic kinetic methods are currently considered to be the most realistic and elegant means of representing and simulating the dynamics of biochemical and biological networks. Deterministic versus stochastic modelling in biochemistry and systems biology introduces and critically reviews the deterministic and stochastic foundations of biochemical kinetics, covering applied stochastic process theory for application in the field of modelling and simulation of biological processes at the molecular scale. Following an overview of deterministic chemical kinetics and the stochastic approach to biochemical kinetics, the book goes onto discuss the specifics of stochastic simulation algorithms, modelling in systems biology and the structure of biochemical models. Later chapters cover reaction-diffusion systems, and provide an analysis of the Kinfer and BlenX software systems. The final chapter looks at simulation of ecodynamics and food web dynamics. Introduces mathematical concepts and formalisms of deterministic and stochastic modelling through clear and simple examples Presents recently developed discrete stochastic formalisms for modelling biological systems and processes Describes and applies stochastic simulation algorithms to implement a stochastic formulation of biochemical and biological kinetics
