Description : This textbook has been developed from the lecture notes for a one-semester course on stochastic modelling. It reviews the basics of probability theory and then covers the following topics: Markov chains, Markov decision processes, jump Markov processes, elements of queueing theory, basic renewal theory, elements of time series and simulation. Rigorous proofs are often replaced with sketches of arguments ? with indications as to why a particular result holds, and also how it is connected with other results ? and illustrated by examples. Wherever possible, the book includes references to more specialised texts containing both proofs and more advanced material related to the topics covered.
Description : This is the expanded second edition of a successful textbook that provides a broad introduction to important areas of stochastic modelling. The original text was developed from lecture notes for a one-semester course for third-year science and actuarial students at the University of Melbourne. It reviewed the basics of probability theory and then covered the following topics: Markov chains, Markov decision processes, jump Markov processes, elements of queueing theory, basic renewal theory, elements of time series and simulation. The present edition adds new chapters on elements of stochastic calculus and introductory mathematical finance that logically complement the topics chosen for the first edition. This makes the book suitable for a larger variety of university courses presenting the fundamentals of modern stochastic modelling. Instead of rigorous proofs we often give only sketches of the arguments, with indications as to why a particular result holds and also how it is related to other results, and illustrate them by examples. Wherever possible, the book includes references to more specialised texts on respective topics that contain both proofs and more advanced material. Request Inspection Copy
Description : Stochastic optimization problems arise in decision-making problems under uncertainty, and find various applications in economics and finance. On the other hand, problems in finance have recently led to new developments in the theory of stochastic control. This volume provides a systematic treatment of stochastic optimization problems applied to finance by presenting the different existing methods: dynamic programming, viscosity solutions, backward stochastic differential equations, and martingale duality methods. The theory is discussed in the context of recent developments in this field, with complete and detailed proofs, and is illustrated by means of concrete examples from the world of finance: portfolio allocation, option hedging, real options, optimal investment, etc. This book is directed towards graduate students and researchers in mathematical finance, and will also benefit applied mathematicians interested in financial applications and practitioners wishing to know more about the use of stochastic optimization methods in finance.
Description : This practical text aims to enable students in engineering, business, operations research, public policy, and computer science to model and analyze stochastic systems. The major classes of useful stochastic processes - discrete and continuous time Markov chains, renewal processes, regenerative processes, and Markov regenerative processes - are presented, with an emphasis on modelling real-life situations with stochastic elements and analyzing the resulting stochastic model.
Description : Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Bayesian inference for complex stochastic models. Re-written to reflect this modern perspective, this second edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context. Keeping with the spirit of the first edition, all of the new theory is presented in a very informal and intuitive manner, keeping the text as accessible as possible to the widest possible readership. New in the Second Edition All examples have been updated to Systems Biology Markup Language Level 3 All code relating to simulation, analysis, and inference for stochastic kinetic models has been re-written and re-structured in a more modular way An ancillary website provides links, resources, errata, and up-to-date information on installation and use of the associated R package More background material on the theory of Markov processes and stochastic differential equations, providing more substance for mathematically inclined readers Discussion of some of the more advanced concepts relating to stochastic kinetic models, such as random time change representations, Kolmogorov equations, Fokker-Planck equations and the linear noise approximation Simple modelling of "extrinsic" and "intrinsic" noise An effective introduction to the area of stochastic modelling in computational systems biology, this new edition adds additional mathematical detail and computational methods that will provide a stronger foundation for the development of more advanced courses in stochastic biological modelling.
Description : Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Bayesian inference for complex stochastic models. Having been thoroughly updated to reflect this, this third edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context. New methods and applications are included in the book, and the use of R for practical illustration of the algorithms has been greatly extended. There is a brand new chapter on spatially extended systems, and the statistical inference chapter has also been extended with new methods, including approximate Bayesian computation (ABC). Stochastic Modelling for Systems Biology, Third Edition is now supplemented by an additional software library, written in Scala, described in a new appendix to the book. New in the Third Edition New chapter on spatially extended systems, covering the spatial Gillespie algorithm for reaction diffusion master equation models in 1- and 2-d, along with fast approximations based on the spatial chemical Langevin equation Significantly expanded chapter on inference for stochastic kinetic models from data, covering ABC, including ABC-SMC Updated R package, including code relating to all of the new material New R package for parsing SBML models into simulatable stochastic Petri net models New open-source software library, written in Scala, replicating most of the functionality of the R packages in a fast, compiled, strongly typed, functional language Keeping with the spirit of earlier editions, all of the new theory is presented in a very informal and intuitive manner, keeping the text as accessible as possible to the widest possible readership. An effective introduction to the area of stochastic modelling in computational systems biology, this new edition adds additional detail and computational methods that will provide a stronger foundation for the development of more advanced courses in stochastic biological modelling.
Description : This fundamental exposition of queueing theory, written by leading researchers, answers the need for a mathematically sound reference work on the subject and has become the standard reference. The thoroughly revised second edition contains a substantial number of exercises and their solutions, which makes the book suitable as a textbook.
Description : This book introduces a novel synthetic paradigm of public health reasoning and epidemic modelling, and then implements it in the study of the infamous 14th century AD Black Death disaster that killed at least one-fourth of the European population. The book starts by focusing on the intellectual context in which epidemic research takes place, in a way that accounts for the interdisciplinary and multicultural trends of the emerging Conceptual Age. The authors maintain that for public health scientists to function in an often complex environment, they should be aware of the divergent conceptions of knowledge and the technological changes that these imply, the multiple and often uncertain databases available and their reliability, the different styles of thinking adopted by the disciplines involved, and the importance of developing sound interdisciplinary knowledge integration skills. A unique feature of the book is that it takes the reader through all four major phases of interdisciplinary inquiry: adequate conceptualization (in terms of metaphors, methodological principles, epistemic rules, and argumentation modes), rigorous formulation (involving sophisticated mathematical models), substantive interpretation (in terms of correspondence principles between form and meaning), and innovative implementation (using advanced systems technology and multi-sourced real world databases). This approach is then applied to scientifically advance the spatiotemporal characterization of the Black Death epidemic, thus going beyond the sensationalistic narration of events found in other publications. The book includes the most complete collection of interdisciplinary information sources available about the Black Death epidemic, each one systematically documented, tabulated, and analyzed. It also presents, for the first time, a series of detailed space-time maps of Black Death mortality, infected area propagation, and epidemic centroid paths throughout the 14th century AD Europe. Preparation of the maps took into account the uncertain nature of the data and integrated a variety of interdisciplinary knowledge bases about the devastating epidemic. These maps provide researchers and the interested public with an informative and substantive description of the Black Death dynamics (temporal evolution, local and global geographical patterns, etc.), and can help one discover an underlying coherence in disease distribution that was buried within reams of contemporary evidence that had so far defied quantitative understanding. The book carefully analyzes the findings of synthetic space-time modelling that enlighten considerably the long-lasting controversy about the nature and origins of the Black Death epidemic. Comparisons are made between the spatiotemporal characteristics of Black Death and bubonic plague, thus contributing to the debate concerning the Black Death etiology. Since Black Death had grave societal, public health, and financial effects, its rigorous study can offer valuable insight into these effects, as well as into similar effects that could result from potential contemporary epidemics.
Description : This book aims to provide a unified treatment of input/output modelling and of control for discrete-time dynamical systems subject to random disturbances. The results presented are of wide applica bility in control engineering, operations research, econometric modelling and many other areas. There are two distinct approaches to mathematical modelling of physical systems: a direct analysis of the physical mechanisms that comprise the process, or a 'black box' approach based on analysis of input/output data. The second approach is adopted here, although of course the properties ofthe models we study, which within the limits of linearity are very general, are also relevant to the behaviour of systems represented by such models, however they are arrived at. The type of system we are interested in is a discrete-time or sampled-data system where the relation between input and output is (at least approximately) linear and where additive random dis turbances are also present, so that the behaviour of the system must be investigated by statistical methods. After a preliminary chapter summarizing elements of probability and linear system theory, we introduce in Chapter 2 some general linear stochastic models, both in input/output and state-space form. Chapter 3 concerns filtering theory: estimation of the state of a dynamical system from noisy observations. As well as being an important topic in its own right, filtering theory provides the link, via the so-called innovations representation, between input/output models (as identified by data analysis) and state-space models, as required for much contemporary control theory.