Probability and statistics

This book arose from our conviction that the NNS-DSGE approach to the analysis of aggregate market outcomes is fundamentally flawed.

This book constitutes the refereed proceedings of the 12th International Conference on Scalable Uncertainty Management, SUM 2018, which was held in Milan, Italy, in October 2018.

Fundamentals of Mathematical Statistics is meant for a standard one-semester advanced undergraduate or graduate-level course in Mathematical Statistics.

This reissue of D.

The Monte Carlo method is a numerical method of solving mathematical problems through random sampling.

Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects ModelsMixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools presents a rigorous framework for describing, implementing, and using mixed effects models.

Searching for a needle in a haystack is an important task in several contexts of data analysis and decision-making.

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework.

This volume teaches readers how to sort through the vast mountain of climate and environmental science data to extract actionable insights.

One of the most important subjects for all engineers and scientists is probability and statistics.

This book presents the foundations of using analytics from the laboratory, social media platforms, and the web.

Statistical Data Analytics Statistical Data Analytics Foundations for Data Mining, Informatics, and Knowledge Discovery A comprehensive introduction to statistical methods for data mining and knowledge discovery Applications of data mining and big data increasingly take center stage in our modern, knowledge-driven society, supported by advances in computing power, automated data acquisition, social media development and interactive, linkable internet software.

As the first book of a three-part series, this book is offered as a tribute to pioneers in vision, such as Bela Julesz, David Marr, King-Sun Fu, Ulf Grenander, and David Mumford.

Accessible to a variety of readers, this book is of interest to specialists, graduate students and researchers in mathematics, optimization, computer science, operations research, management science, engineering and other applied areas interested in solving optimization problems.

Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis.

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The role of the weak convergence technique via weighted empirical processes has proved to be very useful in advancing the development of the asymptotic theory of the so called robust inference procedures corresponding to non-smooth score functions from linear models to nonlinear dynamic models in the 1990's.

Statistics for Business is meant as a textbook for students in business, computer science, bioengineering, environmental technology, and mathematics.

The first edition of this groundbreaking text showed that the Statistical Process Control (SPC) paradigm of W.

Aufgrund einer in den letzten Jahren sprunghaft gewachsenen Verfüg­ barkeit über Rechnerkapazitäten, insbesondere im Bereich der Personal Computer (PC), lassen sich heute auch umfangreiche und aufwendige statistische Datenanalysen innerhalb kürzester Zeit ausführen.

This book delves into the complexities of fault estimation and fault-tolerant control for nonlinear time-delayed systems.

This book constitutes the proceedings of the 15th International Conference on Quantitative Evaluation Systems, QEST 2018, held in Beijing, China, in September 2018.

In this book, we talk about many of the tools and techniques of qual- ity.

Students in the sciences, economics, social sciences, and medicine take an introductory statistics course.

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

This textbook addresses postgraduate students in applied mathematics, probability, and statistics, as well as computer scientists, biologists, physicists and economists, who are seeking a rigorous introduction to applied stochastic processes.

This book introduces the so-called "e;"e;stable factorization approach"e;"e; to the synthesis of feedback controllers for linear control systems.

The book aims to investigate methods and techniques for spatial statistical analysis suitable to model spatial information in support of decision systems.

Comparing Distributions refers to the statistical data analysis that encompasses the traditional goodness-of-fit testing.

Providing the first comprehensive treatment of the subject, this groundbreaking work is solidly founded on a decade of concentrated research, some of which is published here for the first time, as well as practical, 'hands on' classroom experience.

Uncertainty is an inherent feature of both properties of physical systems and the inputs to these systems that needs to be quantified for cost effective and reliable designs.

Data-driven rankings of thousands of history''s most significant people in science, politics, entertainment, and all areas of human endeavor.

This book provides an introduction to computer-based methods for the analysis of genomic data.

Simply stated, this book bridges the gap between statistics and philosophy.

AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications.

This book gives an introduction to discrete-time Markov chains which evolve on a separable metric space.

In this text, modern applied mathematics and physical insight are used to construct the simplest and first nonlinear dynamical model for the Madden-Julian oscillation (MJO), i.

Introduction to the Theory of Optimization in Euclidean Space is intended to provide students with a robust introduction to optimization in Euclidean space, demonstrating the theoretical aspects of the subject whilst also providing clear proofs and applications.

This book discusses stochastic game theory and related concepts.

Stochastic differential equations (SDEs) are a powerful tool in science, mathematics, economics and finance.

This book discusses a link between statistical theory and quantum theory based on the concept of epistemic processes - which can be e.

The European Federation for Medical Informatics has established itself as a regional body coordinating activity in medical informatics.

This monograph, now in a thoroughly revised second edition, offers the latest research on random sets.

People tend to rank values of all kinds linearly from good to bad, but there is little reason to think that this is reasonable or correct.

A First Course in Stochastic Processes focuses on several principal areas of stochastic processes and the diversity of applications of stochastic processes, including Markov chains, Brownian motion, and Poisson processes.