Stochastic Models In The Life Sciences And Their Methods Of Analysis
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Stochastic Models In The Life Sciences And Their Methods Of Analysis
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Author : Frederic Y M Wan
language : en
Publisher: World Scientific
Release Date : 2019-08-29
Stochastic Models In The Life Sciences And Their Methods Of Analysis written by Frederic Y M Wan and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-29 with Mathematics categories.
'… the volume is impressively accessible. The result is a book that is valuable and approachable for biologists at all levels, including those interested in deepening their skills in mathematical modeling and those who seek an overview to aid them in communicating with collaborators in mathematics and statistics. The former group of readers may especially appreciate the first chapter, an introduction to key concepts in probability, and the set of ten assignments provided as an appendix.'CHOICEBiological 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.
Stochastic Models In The Life Sciences And Their Methods Of Analysis
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Author : Frederic Y. M. Wan
language : en
Publisher:
Release Date : 2019
Stochastic Models In The Life Sciences And Their Methods Of Analysis written by Frederic Y. M. Wan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Electronic books categories.
Recent Advances In Stochastic Modeling And Data Analysis
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Author : Christos H Skiadas
language : en
Publisher: World Scientific
Release Date : 2007-11-16
Recent Advances In Stochastic Modeling And Data Analysis written by Christos H Skiadas and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-11-16 with Mathematics categories.
This volume presents the most recent applied and methodological issues in stochastic modeling and data analysis. The contributions cover various fields such as stochastic processes and applications, data analysis methods and techniques, Bayesian methods, biostatistics, econometrics, sampling, linear and nonlinear models, networks and queues, survival analysis, and time series. The volume presents new results with potential for solving real-life problems and provides novel methods for solving these problems by analyzing the relevant data. The use of recent advances in different fields is emphasized, especially new optimization and statistical methods, data warehouse, data mining and knowledge systems, neural computing, and bioinformatics.
Stochastic Models In Biology
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Author : Narendra S. Goel
language : en
Publisher: Elsevier
Release Date : 2013-10-22
Stochastic Models In Biology written by Narendra S. Goel and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-22 with Science categories.
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.
Stochastic Models With Applications To Genetics Cancers Aids And Other Biomedical Systems
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Author : Wai-yuan Tan
language : en
Publisher: World Scientific
Release Date : 2002-02-26
Stochastic Models With Applications To Genetics Cancers Aids And Other Biomedical Systems written by Wai-yuan Tan and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-02-26 with Mathematics categories.
This book presents a systematic treatment of Markov chains, diffusion processes and state space models, as well as alternative approaches to Markov chains through stochastic difference equations and stochastic differential equations. It illustrates how these processes and approaches are applied to many problems in genetics, carcinogenesis, AIDS epidemiology and other biomedical systems.One feature of the book is that it describes the basic MCMC (Markov chain and Monte Carlo) procedures and illustrates how to use the Gibbs sampling method and the multilevel Gibbs sampling method to solve many problems in genetics, carcinogenesis, AIDS and other biomedical systems.As another feature, the book develops many state space models for many genetic problems, carcinogenesis, AIDS epidemiology and HIV pathogenesis. It shows in detail how to use the multilevel Gibbs sampling method to estimate (or predict) simultaneously the state variables and the unknown parameters in cancer chemotherapy, carcinogenesis, AIDS epidemiology and HIV pathogenesis. As a matter of fact, this book is the first to develop many state space models for many genetic problems, carcinogenesis and other biomedical problems.
Recent Development In Stochastic Dynamics And Stochastic Analysis
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Author : Jinqiao Duan
language : en
Publisher: World Scientific
Release Date : 2010
Recent Development In Stochastic Dynamics And Stochastic Analysis written by Jinqiao Duan and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Mathematics categories.
1. Hyperbolic equations with random boundary conditions / Zdzisław Brzeźniak and Szymon Peszat -- 2. Decoherent information of quantum operations / Xuelian Cao, Nan Li and Shunlong Luo -- 3. Stabilization of evolution equations by noise / Tomás Caraballo and Peter E. Kloeden -- 4. Stochastic quantification of missing mechanisms in dynamical systems / Baohua Chen and Jinqiao Duan -- 5. Banach space-valued functionals of white noise / Yin Chen and Caishi Wang -- 6. Hurst index estimation for self-similar processes with long-memory / Alexandra Chronopoulou and Frederi G. Viens -- 7. Modeling colored noise by fractional Brownian motion / Jinqiao Duan, Chujin Li and Xiangjun Wang -- 8. A sufficient condition for non-explosion for a class of stochastic partial differential equations / Hongbo Fu, Daomin Cao and Jinqiao Duan -- 9. The influence of transaction costs on optimal control for an insurance company with a new value function / Lin He, Zongxia Liang and Fei Xing -- 10. Limit theorems for p-variations of solutions of SDEs driven by additive stable Lévy noise and model selection for paleo-climatic data / Claudia Hein, Peter Imkeller and Ilya Pavlyukevich -- 11. Class II semi-subgroups of the infinite dimensional rotation group and associated Lie algebra / Takeyuki Hida and Si Si -- 12. Stopping Weyl processes / Robin L. Hudson -- 13. Karhunen-Loéve expansion for stochastic convolution of cylindrical fractional Brownian motions / Zongxia Liang -- 14. Stein's method meets Malliavin calculus : a short survey with new estimates / Ivan Nourdin and Giovanni Peccati -- 15. On stochastic integrals with respect to an infinite number of Poisson point process and its applications / Guanglin Rang, Qing Li and Sheng You -- 16. Lévy white noise, elliptic SPDEs and Euclidean random fields / Jiang-Lun Wu -- 17. A short presentation of Choquet integral / Jia-An Yan
Stochastic Biomathematical Models
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Author : Mostafa Bachar
language : en
Publisher: Springer
Release Date : 2012-10-19
Stochastic Biomathematical Models written by Mostafa Bachar and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-19 with Mathematics categories.
Stochastic biomathematical models are becoming increasingly important as new light is shed on the role of noise in living systems. In certain biological systems, stochastic effects may even enhance a signal, thus providing a biological motivation for the noise observed in living systems. Recent advances in stochastic analysis and increasing computing power facilitate the analysis of more biophysically realistic models, and this book provides researchers in computational neuroscience and stochastic systems with an overview of recent developments. Key concepts are developed in chapters written by experts in their respective fields. Topics include: one-dimensional homogeneous diffusions and their boundary behavior, large deviation theory and its application in stochastic neurobiological models, a review of mathematical methods for stochastic neuronal integrate-and-fire models, stochastic partial differential equation models in neurobiology, and stochastic modeling of spreading cortical depression.
Stochastic Models Of Tumor Latency And Their Biostatistical Applications
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Author : Andrej Yu Yakovlev
language : en
Publisher: World Scientific
Release Date : 1996
Stochastic Models Of Tumor Latency And Their Biostatistical Applications written by Andrej Yu Yakovlev and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Medical categories.
This research monograph discusses newly developed mathematical models and methods that provide biologically meaningful inferences from data on cancer latency produced by follow-up and discrete surveillance studies. Methods for designing optimal strategies of cancer surveillance are systematically presented for the first time in this book. It offers new approaches to the stochastic description of tumor latency, employs biologically-based models for making statistical inference from data on tumor recurrence and also discusses methods of statistical analysis of data resulting from discrete surveillance strategies. It also offers insight into the role of prognostic factors based on the interpretation of their effects in terms of parameters endowed with biological meaning, as well as methods for designing optimal schedules of cancer screening and surveillance. Last but not least, it discusses survival models allowing for cure rates and the choice of optimal treatment based on covariate information, and presents numerous examples of real data analysis.
Methods And Models In Mathematical Biology
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Author : Johannes Müller
language : en
Publisher: Springer
Release Date : 2015-08-13
Methods And Models In Mathematical Biology written by Johannes Müller and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-13 with Mathematics categories.
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 Modelling Of Social Processes
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Author : Andreas Diekmann
language : en
Publisher: Academic Press
Release Date : 2014-05-10
Stochastic Modelling Of Social Processes written by Andreas Diekmann and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-10 with Mathematics categories.
Stochastic Modelling of Social Processes provides information pertinent to the development in the field of stochastic modeling and its applications in the social sciences. This book demonstrates that stochastic models can fulfill the goals of explanation and prediction. Organized into nine chapters, this book begins with an overview of stochastic models that fulfill normative, predictive, and structural–analytic roles with the aid of the theory of probability. This text then examines the study of labor market structures using analysis of job and career mobility, which is one of the approaches taken by sociologists in research on the labor market. Other chapters consider the characteristic trends and patterns from data on divorces. This book discusses as well the two approaches of stochastic modeling of social processes, namely competing risk models and semi-Markov processes. The final chapter deals with the practical application of regression models of survival data. This book is a valuable resource for social scientists and statisticians.