Computational Probability Applications (International Series in Operations Research & Management Science, #247)
This focuses on the developing field of building probability models with the power of symbolic algebra systems. The book combines the uses of symbolic algebra with probabilistic/stochastic application and highlights the applications in a variety of contexts. The research explored in each chapter is unified by the use of A Probability Programming Language (APPL) to achieve the modeling objectives. APPL, as a research tool, enables a probabilist or statistician the ability to explore new ideas, me...
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs. The book’s main features are as follows:The content is written in an easy-to-follow and self-contained style.The book includes 100 exercises, which have been carefully selected and refined....
This text covers topics such as nonparametric statistics, statistical quality control, multivariate regression analysis and operating characteristic curves. The accompanying IBM software gives a complete treatment of statistically valid sample sizes in all tests of hypotheses addressed.
The Rise of Artificial Intelligence and Big Data in Pandemic Society
by Kazuhiko Shibuya
This book presents a study of the COVID-19 pandemic using computational social scientific analysis that draws from, and employs, statistics and simulations. Combining approaches in crisis management, risk assessment and mathematical modelling, the work also draws from the philosophy of sacrifice and futurology. It makes an original contribution to the important issue of the stability of society by highlighting two significant factors: the COVID-19 crisis as a catalyst for change and the rise of...
Dirichlet Forms and Related Topics (Springer Proceedings in Mathematics & Statistics, #394)
This conference proceeding contains 27 peer-reviewed invited papers from leading experts as well as young researchers all over the world in the related fields that Professor Fukushima has made important contributions to. These 27 papers cover a wide range of topics in probability theory, ranging from Dirichlet form theory, Markov processes, heat kernel estimates, entropy on Wiener spaces, analysis on fractal spaces, random spanning tree and Poissonian loop ensemble, random Riemannian geometry,...
An Introduction to Sequential Monte Carlo (Springer Series in Statistics)
by Nicolas Chopin and Omiros Papaspiliopoulos
This book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters. These methods have become a staple for the sequential analysis of data in such diverse fields as signal processing, epidemiology, machine learning, population ecology, quantitative finance, and robotics. The coverage is comprehensive, ranging from the underlying theory to computational implementation, methodology, and diverse applications in various areas of science. This is achiev...
Statistik Fur Okonomen (Springer-Lehrbuch)
by Wolfgang Kohn and Riza Ozturk
Das Buch richtet sich an diejenigen, die Statistik in wirtschaftswissenschaftlich orientierten Studiengangen studieren. Der leicht verstandliche Text ist mit vielen Beispielen und Ubungen erganzt. Die praxisnahe Darstellung der Methoden wird durch die Erklarung und Anwendung der Statistikprogramme R (open source Progamm) und SPSS vervollstandigt. Im Text sind fur beide Programme viele Programmanweisungen enthalten. Die Autoren haben kompakt alle elementaren statistischen Verfahren fur die Okonom...
Java Methods for Financial Engineering (Springer Professional Computing)
by Philip Barker
This book describes the principles of model building in financial engineering. It explains those models as designs and working implementations for Java-based applications. The book provides software professionals with an accessible source of numerical methods or ready-to-use code for use in business applications. It is the first book to cover the topic of Java implementations for finance/investment applications and is written specifically to be accessible to software practitioners without prior...
With the development of computing technologies in today's modernized world, software packages have become easily accessible. Open source software, specifically, is a popular method for solving certain issues in the field of computer science. One key challenge is analyzing big data due to the high amounts that organizations are processing. Researchers and professionals need research on the foundations of open source software programs and how they can successfully analyze statistical data. Open So...
A guide to the various statistical techniques avaiable with SPSS Advanced Statistics and how to obtain the appropriate statistical analysis with dialog box interface. A reference guide provides syntax for all SPSS Advanced Statistics commands. Statistical procedures in this module include: general linear model; multivariate analysis of variance, loglinear, hierachical loglinear, genlog; and survival.
Conditional Independence and Linear Programming (SpringerBriefs in Statistics) (JSS Research Series in Statistics)
by Kentaro Tanaka
This book is the first to be devoted to the fusion between statistical causal inference and mathematical programming. The main purpose of the book is to provide the algorithms for solving the implication problem of conditional independence statements by using a computer. The concept of conditional independence is very much tied to the factorization of graphical models; hence it is very important to know the rules of conditional independence. Beginning with a brief introduction to linear programm...
Statistical Modeling and Computation
by Dirk P. Kroese and Joshua C.C. Chan
This textbook on statistical modeling and statistical inference will assist advanced undergraduate and graduate students. Statistical Modeling and Computation provides a unique introduction to modern Statistics from both classical and Bayesian perspectives. It also offers an integrated treatment of Mathematical Statistics and modern statistical computation, emphasizing statistical modeling, computational techniques, and applications. Each of the three parts will cover topics essential to univers...
The Calabi–Yau Landscape (Lecture Notes in Mathematics, #2293)
by Yang-Hui He
Can artificial intelligence learn mathematics? The question is at the heart of this original monograph bringing together theoretical physics, modern geometry, and data science. The study of Calabi–Yau manifolds lies at an exciting intersection between physics and mathematics. Recently, there has been much activity in applying machine learning to solve otherwise intractable problems, to conjecture new formulae, or to understand the underlying structure of mathematics. In this book, insights from...
Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, you'll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts. The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what you've...