This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences.
This book constitutes the refereed proceedings of the 22nd International Conference on Distributed and Computer and Communication Networks, DCCN 2019, held in Moscow, Russia, in September 2019.
Mikhail Zhilkin, a data scientist who has worked on projects ranging from Candy Crush games to Premier League football players' physical performance, shares his strong views on some of the best and, more importantly, worst practices in data analytics and business intelligence.
Dieses Lehrbuch wendet sich hauptsächlich an Studierende der Ingenieur- und Naturwissenschaften sowie der Informatik, aber auch an in der angewandten Praxis tätige Absolventen dieser Disziplinen.
Disease Mapping: From Foundations to Multidimensional Modeling guides the reader from the basics of disease mapping to the most advanced topics in this field.
This book addresses the most important aspects of how to plan and evaluate clinical trials with a composite primary endpoint to guarantee a clinically meaningful and valid interpretation of the results.
Presenting the latest findings in topics from across the mathematical spectrum, this volume includes results in pure mathematics along with a range of new advances and novel applications to other fields such as probability, statistics, biology, and computer science.
THE COMPLETE COLLECTION NECESSARY FOR A CONCRETE UNDERSTANDING OF PROBABILITY Written in a clear, accessible, and comprehensive manner, the Handbook of Probability presents the fundamentals of probability with an emphasis on the balance of theory, application, and methodology.
Despite the proliferation of video games in the twenty-first century, the theory of game design is largely underdeveloped, leaving designers on their own to understand what games really are.
This book is a comprehensive introduction to financial modeling that teaches advanced undergraduate and graduate students in finance and economics how to use R to analyze financial data and implement financial models.
Statistics: The Art and Science of Learning From Data, 5th Edition helps you understand what statistics is all about and learn the right questions to ask when analyzing data, instead of just memorizing procedures.
Besides a number of papers on classical areas of research in probability such as martingale theory, Malliavin calculus and 2-parameter processes, this new volume of the Séminaire de Probabilités develops the following themes: - chaos representation for some new kinds of martingales, - quantum probability, - branching aspects on Brownian excursions, - Brownian motion on a set of rays.
This book constitutes the refereed proceedings of the 18th European Workshop on Computer Performance Engineering, EPEW 2022, held in Santa Pola, Spain, in September 2022.
This book is for people who work in the tech industry-computer and data scientists, software developers and engineers, designers, and people in business, marketing or management roles.
Reliability Modelling and Analysis in Discrete Time provides an overview of the probabilistic and statistical aspects connected with discrete reliability systems.
A one-stop shop for actuaries and risk managers, this handbook covers general solvency and risk management topics as well issues pertaining to the European Solvency II project.
The aim of this book is to equip biostatisticians and other quantitative scientists with the necessary skills, knowledge, and habits to collaborate effectively with clinicians in the healthcare field.
This advanced undergraduate/graduate textbook teaches students in finance and economics how to use R to analyse financial data and implement financial models.
Thoroughly updated throughout, A First Course in Linear Model Theory, Second Edition is an intermediate-level statistics text that fills an important gap by presenting the theory of linear statistical models at a level appropriate for senior undergraduate or first-year graduate students.
This book presents a general method for deriving higher-order statistics of multivariate distributions with simple algorithms that allow for actual calculations.
The confluence of big data, artificial intelligence (AI), and machine learning (ML) has led to a paradigm shift in how innovative medicines are developed and healthcare delivered.
While there are several texts on how to solve and analyze stochastic programs, this is the first text to address basic questions about how to model uncertainty, and how to reformulate a deterministic model so that it can be analyzed in a stochastic setting.
This book delves into the intricate world of interval programming, offering a comprehensive exploration of mathematical programming problems characterized by interval data.
Das vorliegende Buch gibt einen Überblick über dieGrundlagen der Schadenversicherungsmathematik: Risikomodelle, Tarifierung,Reservierung, Risikoteilung.
