An Advanced Course in Probability and Stochastic Processes provides a modern and rigorous treatment of probability theory and stochastic processes at an upper undergraduate and graduate level.
Petri nets model concurrent and distributed systems where active components communicate through the production and absorption of various kinds of resources.
The goal of the 2019 conference on Stochastic Processes and Algebraic Structures held in SPAS2019, Vasteras, Sweden, from September 30th to October 2nd 2019 was to showcase the frontiers of research in several important topics of mathematics, mathematical statistics, and its applications.
This volume provides an overview of two of the most important examples of interacting particle systems, the contact process, and the voter model, as well as their many variants introduced in the past 50 years.
International Series of Monographs in Pure and Applied Mathematics, Volume 103: Stochastic Theory of Service Systems focuses on the principles, methodologies, and approaches involved in the stochastic theory of service systems.
Vector Quantization, a pioneering discretization method based on nearest neighbor search, emerged in the 1950s primarily in signal processing, electrical engineering, and information theory.
Artificial Intelligence, Volume 49 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics.
Artificial Intelligence, Volume 49 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics.
This book contains contributions from the participants of the international conference "e;Foundations of Modern Statistics"e; which took place at Weierstrass Institute for Applied Analysis and Stochastics (WIAS), Berlin, during November 6-8, 2019, and at Higher School of Economics (HSE University), Moscow, during November 30, 2019.
This is an updated version of what is still the only text to address basic questions about how to model uncertainty in mathematical programming, including how to reformulate a deterministic model so that it can be analyzed in a stochastic setting.
This book presents the theory of rational decisions involving the selection of stopping times in observed discrete-time stochastic processes, both by single and multiple decision-makers.
The long-awaited revision of Fundamentals of Applied Probability and Random Processes expands on the central components that made the first edition a classic.
This book constitutes an up-to-date account of principles, methods, and tools for mathematical and statistical modelling in a wide range of research fields, including medicine, health sciences, biology, environmental science, engineering, physics, chemistry, computation, finance, economics, and social sciences.
Deep Learning, Volume 48 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Generative Adversarial Networks for Biometric Synthesis, Data Science and Pattern Recognition, Facial Data Analysis, Deep Learning in Electronics, Pattern Recognition, Computer Vision and Image Processing, Mechanical Systems, Crop Technology and Weather, Manipulating Faces for Identity Theft via Morphing and Deepfake, Biomedical Engineering, and more.
This book presents in a compact form the program carried out in introductory statistics courses and discusses some essential topics for research activity, such as Monte Carlo simulation techniques, methods of statistical inference, best fit and analysis of laboratory data.
Dieses Lehrbuch wendet sich hauptsächlich an Studierende der Ingenieur- und Naturwissenschaften sowie der Informatik, aber auch an in der angewandten Praxis tätige Fachkräfte in diesen Disziplinen.
This book provides analytic tools to describe local and global behavior of solutions to Ito-stochastic differential equations with non-degenerate Sobolev diffusion coefficients and locally integrable drift.
Basierend auf Grundkenntnissen aus der Schulzeit oder aus dem ersten Band des Gesamtwerks „Mathematik verstehen und anwenden“ führt dieser zweite Band in die Vektoranalysis, in das Gebiet der Differenzialgleichungen und in die Fourier-Analysis einschließlich der Laplace-Transformation ein und beinhaltet außerdem eine Einführung in die Wahrscheinlichkeitsrechnung und Statistik.
