Gaussian processes can be viewed as a far-reaching infinite-dimensional extension of classical normal random variables. Their theory presents a powerful range of tools for probabilistic modelling in various academic and technical domains such as Statistics, Forecasting, Finance, Information Transmission, Machine Learning - to mention just a few. The objective of these Briefs is to present a quick and condensed treatment of the core theory that a reader must understand in order to make his own independent contributions. The primary intended readership are PhD/Masters students and researchers working in pure or applied mathematics. The first chapters introduce essentials of the classical theory of Gaussian processes and measures with the core notions of reproducing kernel, integral representation, isoperimetric property, large deviation principle. The brevity being a priority for teaching and learning purposes, certain technical details and proofs are omitted. The later chapters touch important recent issues not sufficiently reflected in the literature, such as small deviations, expansions, and quantization of processes. In university teaching, one can build a one-semester advanced course upon these Briefs.​

"Mastering Forecasting in Machine Learning: A Journey through Crude Oil and Beyond" navigates the complex world of forecasting with engaging dialogues. From peeling layers in Chapter 1 to deploying predictive models in Chapter 11, the book demystifies the process. It explores data gathering, feature engineering, algorithmic magic, and model validation.

With the pressure of time-based competition increasing, and customers demanding faster service, availability of service parts becomes a critical component of manufacturing and servicing operations. Service Parts Management first focuses on intermittent demand forecasting and then on the management of service parts inventories. It guides researchers and practitioners in finding better management solutions to their problems and is both an excellent reference for key concepts and a leading resource for further research. Demand forecasting techniques are presented for parametric and nonparametric approaches, and multi echelon cases and inventory pooling are also considered. Inventory control is examined in the continuous and periodic review cases, while the following are all examined in the context of forecasting: • error measures, • distributional assumptions, and • decision trees. Service Parts Management provides the reader with an overview and a detailed treatment of the current state of the research available on the forecasting and inventory management of items with intermittent demand. It is a comprehensive review of service parts management and provides a starting point for researchers, postgraduate students, and anyone interested in forecasting or managing inventory.

This book focus on creating popularity and interest in modeling, derivation of equations for plant disease forecasting or construction and use of Web-based Expert Systems among plant pathologists. This book covers descriptions of many historic plant disease epidemics, various forecasting systems and methods of their construction, instruments required for study of plant disease epidemics, widely used commercial forecasting systems and present global scenario of forecasting.

In the human history plant disease epidemics have brought unsurmountable misery many a times. Still breaking out of epidemic in any time in any part of the world is a stark reality. The panic spraying of chemical pesticides is not a panacea. Only the IPM technology may give relief. This technology if backed by the disease forewarning system may yield the desired results. Hence, an in depth understanding of plant disease forecasting system and its successful implementation may bring the global food security.

This title provides a useful background for all students, practitioners, and researchers interested in the field of epidemiology, food security and agriculture sciences.

This volume is a compilation of nine chapters covering aspects of tectonics, seismicity, earthquake forecasting, geotechnical engineering, and buildings. It presents state-of-the-art techniques for calculating moment tensors, rupture inversions, and hypocentral locations.

This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time.