The series is devoted to the publication of high-level monographs and surveys which cover the whole spectrum of probability and statistics. The books of the series are addressed to both experts and advanced students.
New up-to-date edition of this influential classic on Markov chains in general state spaces. Proofs are rigorous and concise, the range of applications is broad and knowledgeable, and key ideas are accessible to practitioners with limited mathematical background. New commentary by Sean Meyn, including updated references, reflects developments since 1996.
Since the publication of the first edition of the present volume in 1980, the stochastic stability of differential equations has become a very popular subject of research in mathematics and engineering. To date exact formulas for the Lyapunov exponent, the criteria for the moment and almost sure stability, and for the existence of stationary and periodic solutions of stochastic differential equations have been widely used in the literature. In this updated volume readers will find important new results on the moment Lyapunov exponent, stability index and some other fields, obtained after publication of the first edition, and a significantly expanded bibliography. This volume provides a solid foundation for students in graduate courses in mathematics and its applications. It is also useful for those researchers who would like to learn more about this subject, to start their research in this area or to study the properties of concrete mechanical systems subjected to random perturbations.
This brief modern introduction to the subject of ordinary differential equations emphasizes stability theory. Concisely and lucidly expressed, it is intended as a supplementary text for advanced undergraduates or beginning graduate students who have completed a first course in ordinary differential equations. The author begins by developing the notions of a fundamental system of solutions, the Wronskian, and the corresponding fundamental matrix. Subsequent chapters explore the linear equation with constant coefficients, stability theory for autonomous and nonautonomous systems, and the problems of the existence and uniqueness of solutions and related topics. Problems at the end of each chapter and two Appendixes on special topics enrich the text.
This book constitutes the refereed proceedings of the 8th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2017, held in Faro, Portugal, in June 2017. The 60 regular papers presented in this volume were carefully reviewed and selected from 86 submissions. They are organized in topical sections named: Pattern Recognition and Machine Learning; Computer Vision; Image and Signal Processing; Medical Image; and Applications.
Now available in a fully revised and updated second edition, this well established textbook provides a straightforward introduction to the theory of probability. The presentation is entertaining without any sacrifice of rigour; important notions are covered with the clarity that the subject demands. Topics covered include conditional probability, independence, discrete and continuous random variables, basic combinatorics, generating functions and limit theorems, and an introduction to Markov chains. The text is accessible to undergraduate students and provides numerous worked examples and exercises to help build the important skills necessary for problem solving.
Suitable for advanced undergraduates and graduate students, this was the first English-language text to offer detailed coverage of boundedness, stability, and asymptotic behavior of linear and nonlinear differential equations. It remains a classic guide, featuring material from original research papers, including the author's own studies. The linear equation with constant and almost-constant coefficients receives in-depth attention that includes aspects of matrix theory. No previous acquaintance with the theory is necessary, since author Richard Bellman derives the results in matrix theory from the beginning. In regard to the stability of nonlinear systems, results of the linear theory are used to drive the results of Poincaré and Liapounoff. Professor Bellman then surveys important results concerning the boundedness, stability, and asymptotic behavior of second-order linear differential equations. The final chapters explore significant nonlinear differential equations whose solutions may be completely described in terms of asymptotic behavior. Only real solutions of real equations are considered, and the treatment emphasizes the behavior of these solutions as the independent variable increases without limit.
Probability has fascinated philosophers, scientists, and mathematicians for hundreds of years. Although the mathematics of probability is, for most applications, clear and uncontroversial, the interpretation of probability statements continues to be fraught with controversy and confusion. What does it mean to say that the probability of some event X occurring is 31%? In the 20th century a consensus emerged that there are at least two legitimate kinds of probability, and correspondingly at least two kinds of possible answers to this question of meaning. Subjective probability, also called 'credence' or 'degree of belief' is a numerical measure of the confidence of some person or some ideal rational agent. Objective probability, or chance, is a fact about how things are in the world. It is this second type of probability with which Carl Hoefer is concerned in this volume, specifically how we can understand the meaning of statements about objective probability. He aims to settle the question of what objective chances are, once and for all, with an account that can meet the demands of philosophers and scientists alike. For Hoefer, chances are constituted by patterns that can be discerned in the events that happen in our world. These patterns are ideally appropriate guides to what credences limited rational agents, such as ourselves, should have in situations of imperfect knowledge. By showing this, Hoefer bridges the gap between subjective probability and chance. In a field where few scholars have given adequate treatment to interpreting statements of chance, Hoefer develops a philosophically rich theory which draws on the disciplines of metaphysics, ontology, and philosophy of science.
Published in 1999. How can we reconcile assumptions about the lawfulness of the universe with provision for chance events? Do the ‘laws of nature’ indicate what absolutely must happen, or just what is most likely to happen? These are important questions for both science and theology, and are explored here in the first in-depth coverage of an important but neglected topic. Including perspectives from prestigious contributions, and published with the backing of the International Society for Science and Religion (ISSR), Creation: Law and Probability employs the disciplines of history and philosophy, as well as cosmology, evolutionary biology, and neuroscience in a fascinating dialogue of faith traditions.