Applied Stochastic Processes is a collection of papers dealing with stochastic processes, stochastic equations, and their applications in many fields of science.
A strong grasp of elementary statistics and probability, along with basic skills in using R, is essential for various scientific disciplines reliant on data analysis.
This classroom-tested textbook is an introduction to probability theory, with the right balance between mathematical precision, probabilistic intuition, and concrete applications.
Seit seinem Erscheinen hat sich das Buch umgehend als Standardwerk für eine umfassende und moderne Einführung in die Wahrscheinlichkeitstheorie und ihre maßtheoretischen Grundlagen etabliert.
Intended for advanced undergraduates and graduate students, this book is a practical guide to the use of probability and statistics in experimental physics.
This book provides an extensive coverage of the methodology of survival analysis, ranging from introductory level material to deeper more advanced topics.
This monograph explores the interdisciplinary applications of information theory, focusing on the concepts of entropy, mutual information, and their implications in various fields.
Steps forward in mathematics often reverberate in other scientific disciplines, and give rise to innovative conceptual developments or find surprising technological applications.
This work is devoted to several problems of parametric (mainly) and nonparametric estimation through the observation of Poisson processes defined on general spaces.
Modeling Uncertainty: An Examination of Stochastic Theory, Methods, and Applications, is a volume undertaken by the friends and colleagues of Sid Yakowitz in his honor.
From the reviews of the First Edition: "e;This excellent book is based on several sets of lecture notes written over a decade and has its origin in a one-semester course given by the author at the ETH, Zurich, in the spring of 1970.
Time Series Analysis With Applications in R, Second Edition, presents an accessible approach to understanding time series models and their applications.
Petri nets model concurrent and distributed systems where active components communicate through the production and absorption of various kinds of resources.
This book provides a foundation in stochastic, linear, and mixed-integer programming algorithms with a focus on practical computer algorithm implementation.
This book aims to give a complete and self-contained presentation of semi- Markov models with finitely many states, in view of solving real life problems of risk management in three main fields: Finance, Insurance and Reliability providing a useful complement to our first book (Janssen and Manca (2006)) which gives a theoretical presentation of semi-Markov theory.
Applied Stochastic Models and Control for Finance and Insurance presents at an introductory level some essential stochastic models applied in economics, finance and insurance.
Conceptual Econometrics Using R, Volume 41 provides state-of-the-art information on important topics in econometrics, including quantitative game theory, multivariate GARCH, stochastic frontiers, fractional responses, specification testing and model selection, exogeneity testing, causal analysis and forecasting, GMM models, asset bubbles and crises, corporate investments, classification, forecasting, nonstandard problems, cointegration, productivity and financial market jumps and co-jumps, among others.
