Probability and statistics

This volume is the first of two containing selected papers from the International Conference on Advances in Mathematical Sciences, Vellore, India, December 2017 - Volume I.

This book brings together carefully selected, peer-reviewed works on mathematical biology presented at the BIOMAT International Symposium on Mathematical and Computational Biology, which was held at the Institute of Numerical Mathematics, Russian Academy of Sciences, in October 2017, in Moscow.

This book constitutes the thoroughly refereed proceedings of the 25th International Conference on Computer Networks, CN 2018, held in Gliwice, Poland, in June 2018.

This 49th volume offers a good sample of the main streams of current research on probability and stochastic processes, in particular those active in France.

This highly-readable text provides grounds on how to plan and conduct animal experiments that can be reproduced by others.

This book presents an overview of computational and statistical design and analysis of mass spectrometry-based proteomics, metabolomics, and lipidomics data.

This book constitutes the proceedings of the 25th International Conference on Information Processing in Medical Imaging, IPMI 2017, held at the Appalachian State University, Boon, NC, USA, in June 2017.

This book constitutes the thoroughly refereed proceedings of the 24th International Conference on Computer Networks, CN 2017, held in Brunow, Poland, in June 2017.

The book provides a description of the process of health economic evaluation and modelling for cost-effectiveness analysis, particularly from the perspective of a Bayesian statistical approach.

The volume describes the main theoretical propositions of the methodology to predict mechanical reliability under conditions of repeated exposure to random extreme loads.

This book describes why conventional methods fall short to solve the comparability problem and introduces three successive innovations to overcome these shortcomings.

This book explores inductive inference using the minimum message length (MML) principle, a Bayesian method which is a realisation of Ockham's Razor based on information theory.

This book shows the capabilities of Microsoft Excel in teaching environmental science statistics effectively.

Risk is the main source of uncertainty for investors, debtholders, corporate managers and other stakeholders.

This concise book for practitioners presents the statistical analysis of operational risk, which is considered the most relevant source of bank risk, after market and credit risk.

These proceedings from the 37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2017), held in Sao Carlos, Brazil, aim to expand the available research on Bayesian methods and promote their application in the scientific community.

This textbook is a practical yet in depth guide to cryptography and its principles and practices.

This proceedings book presents state-of-the-art developments in theory, methodology, and applications of network analysis across sociology, computational science, education research, literature studies, political science, international relations, social media research, and urban studies.

This book contains a selection of the best papers of the 31st Benelux Conference on Artificial Intelligence, BNAIC 2019, and 28th Belgian Dutch Machine Learning Conference, BENELEARN 2019, held in Brussels, Belgium in November 2019.

This book is the second edition of Facet Theory and the Mapping Sentence: Evolving Philosophy, Use and Application (2014).

Dieses Buch soll Studierende als auch Doktoranden motivieren, eigene Studien mit dem richtigen Untersuchungsdesign zu planen und durchzuführen.

This graduate-level textbook is a tutorial for item response theory that covers both the basics of item response theory and the use of R for preparing graphical presentation in writings about the theory.

This textbook on computational statistics presents tools and concepts of univariate and multivariate statistical data analysis with a strong focus on applications and implementations in the statistical software R.

This volume of selected and peer-reviewed contributions on the latest developments in time series analysis and forecasting updates the reader on topics such as analysis of irregularly sampled time series, multi-scale analysis of univariate and multivariate time series, linear and non-linear time series models, advanced time series forecasting methods, applications in time series analysis and forecasting, advanced methods and online learning in time series and high-dimensional and complex/big data time series.

This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks.

This book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters.

This book provides readers with a systematic approach to quantitative investments and bridges the gap between theory and practice, equipping students to more seamlessly enter the world of industry.

This book on counting statistics presents a novel copula-based approach to counting dependent random events.

This book is specially designed to refresh and elevate the level of understanding of the foundational background in probability and distributional theory required to be successful in a graduate-level statistics program.

This book gathers the most essential results, including recent ones, on linear-quadratic optimal control problems, which represent an important aspect of stochastic control.

This book provides an introduction to quantitative marketing with Python.

This advanced undergraduate/graduate textbook teaches students in finance and economics how to use R to analyse financial data and implement financial models.

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This book covers numerical methods for stochastic partial differential equations with white noise using the framework of Wong-Zakai approximation.

This book provides hands-on guidance for researchers and practitioners in criminal justice and criminology to perform statistical analyses and data visualization in the free and open-source software R.

The two volume set LNCS 10424 and 10425 constitutes the refereed proceedings of the 17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017, held in Ystad, Sweden, in August 2017.

Die Sozialwissenschaftler Florian G.

This two-volume book offers a comprehensive treatment of the probabilistic approach to mean field game models and their applications.

Presenting some recent results on the construction and the moments of Levy-type processes, the focus of this volume is on a new existence theorem, which is proved using a parametrix construction.

The sciences philosophy, psychology and neuroscience share the basis that all refer to the human being.

This brief describes the basics of Riemannian optimization-optimization on Riemannian manifolds-introduces algorithms for Riemannian optimization problems, discusses the theoretical properties of these algorithms, and suggests possible applications of Riemannian optimization to problems in other fields.

This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science.

This book examines what seems to be the basic challenge in neuroscience today: understanding how experience generated by the human brain is related to the physical world we live in.

This volume presents new methods and applications in longitudinal data estimation methodology in applied economic.

This book is aimed at researchers, graduate students and engineers who would like to be initiated to Piecewise Deterministic Markov Processes (PDMPs).

This book generalizes and extends the available theory in robust and decentralized hypothesis testing.

Modelling trends and cycles in economic time series has a long history, with the use of linear trends and moving averages forming the basic tool kit of economists until the 1970s.

The main focus of this book is on presenting advances in fuzzy statistics, and on proposing a methodology for testing hypotheses in the fuzzy environment based on the estimation of fuzzy confidence intervals, a context in which not only the data but also the hypotheses are considered to be fuzzy.