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.
Sergio Albeverio gave important contributions to many fields ranging from Physics to Mathematics, while creating new research areas from their interplay.
Dieses Lehrbuch führt in das faszinierende Gebiet der stochastischen Prozesse ein, indem es die entsprechenden Inhalte verständlich darstellt und sie mit Anwendungen aus den Natur- und Ingenieurwissenschaften verbindet.
This text provides a concise introduction, suitable for a one-semester special topicscourse, to the remarkable properties of Gaussian measures on both finite and infinitedimensional spaces.
This book introduces the concept of "e;bespoke learning"e;, a new mechanistic approach that makes it possible to generate values of an output variable at each designated value of an associated input variable.
This book provides a compact introduction to the theory of measure-valued branching processes, immigration processes and Ornstein-Uhlenbeck type processes.
This book is designed to be an introductory course to some basic chapters of Advanced Mathematics for Engineering and Physics students, researchers in different branches of Applied Mathematics and anyone wanting to improve their mathematical knowledge by a clear, live, self-contained and motivated text.
This book is a detailed introduction to selective maintenance and updates readers on recent advances in this field, emphasizing mathematical formulation and optimization techniques.
The sixth edition provides expanded Discussion and Comments and References sections at the end of each chapter, creating a spotlight on practical applications of the theory presented in that chapter.
This textbook will continue to be the best suitable textbook written specifically for a first course on probability theory and designed for industrial engineering and operations management students.
This textbook will continue to be the best suitable textbook written specifically for a first course on probability theory and designed for industrial engineering and operations management students.
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 milestone 50th volume of the "e;Seminaire de Probabilites"e; pays tribute with a series of memorial texts to one of its former editors, Jacques Azema, who passed away in January.
This undergraduate textbook presents an inquiry-based learning course in stochastic models and computing designed to serve as a first course in probability.
