Probability and statistics

Practical Numerical and Scientific Computing with MATLAB(R) and Python concentrates on the practical aspects of numerical analysis and linear and non-linear programming.

This book helps students, researchers, and practicing engineers to understand the theoretical framework of control and system theory for discrete-time stochastic systems so that they can then apply its principles to their own stochastic control systems and to the solution of control, filtering, and realization problems for such systems.

This book contains the proceedings of the conference "e;Fractals in Graz 2001 - Analysis, Dynamics, Geometry, Stochastics"e; that was held in the second week of June 2001 at Graz University of Technology, in the capital of Styria, southeastern province of Austria.

Simplifies the treatment of statistical inference focusing on how to specify and interpret models in the context of testing causal theories.

With ever-rising healthcare costs, evidence generation through Health Economics and Outcomes Research (HEOR) plays an increasingly important role in decision-making about the allocation of resources.

This work describes several statistical techniques for studying repeated measures data, presenting growth curve methods applicable to biomedical, social, animal, agricultural and business research.

Philosophy of Probability provides a comprehensive introduction to theoretical issues that occupy a central position in disciplines ranging from philosophy of mind and epistemology to cognitive science, decision theory and artificial intelligence.

Now updated in a valuable new edition this user-friendly book focuses on understanding the "e;why"e; of mathematical statistics Probability and Statistical Inference, Second Edition introduces key probability and statis-tical concepts through non-trivial, real-world examples and promotes the developmentof intuition rather than simple application.

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective.

The Mathematics of Errors presents an original, rigorous and systematic approach to the calculus of errors, targeted at both the engineer and the mathematician.

Praise for the First Edition"e;The main strength of this book is that it provides a unified framework of graphical tools for data analysis, especially for univariate and low-dimensional multivariate data.

Probability, Random Variables, and Random Processes is a comprehensive textbook on probability theory for engineers that provides a more rigorous mathematical framework than is usually encountered in undergraduate courses.

A beginner s guide to stochastic growth modeling The chief advantage of stochastic growth models over deterministic models is that they combine both deterministic and stochastic elements of dynamic behaviors, such as weather, natural disasters, market fluctuations, and epidemics.

Astronomical photographs contain an enormous amount of information.

Powerful, Flexible Tools for a Data-Driven WorldAs the data deluge continues in today's world, the need to master data mining, predictive analytics, and business analytics has never been greater.

Stochastic Optimal Control (SOC)-a mathematical theory concerned with minimizing a cost (or maximizing a payout) pertaining to a controlled dynamic process under uncertainty-has proven incredibly helpful to understanding and predicting debt crises and evaluating proposed financial regulation and risk management.

This book reflects on 8 decades of research on one of the longest-standing unsolved problems in modern astrophysics: why does the Sun form a hot corona?

This book presents and standardizes statistical models and methods that can be directly applied to both reliability and survival analysis.

This book offers a comprehensive and systematic review of the latest research findings in the area of intuitionistic fuzzy calculus.

A step-by-step guide to the design and implementation of surveys.

An Introduction to Algebraic and Combinatorial Coding Theory focuses on the principles, operations, and approaches involved in the combinatorial coding theory, including linear transformations, chain groups, vector spaces, and combinatorial constructions.

This volume is a tribute to Professor Dietrich von Rosen on the occasion of his 65th birthday.

An update of this popular introduction to probability theory and information theory with new material on Markov chains.

This book constitutes a review of recent developments in the theory and practical exploitation of the elliptical model for measured data in both classical and emerging areas of signal processing.

Ziel dieses Übungsbuches ist es, den Studierenden eine umfassende Möglichkeit zu geben, ihre bereits erworbenen Statistikkenntnisse intensiv zu nutzen und zu vertiefen.

Economic evaluation has become an essential component of clinical trial design to show that new treatments and technologies offer value to payers in various healthcare systems.

The book presents contributions on statistical models and methods applied, for both data science and SDGs, in one place.

This book was written to aid quality technicians and engineers.

This book details cutting-edge research into human-like driving technology, utilising game theory to better suit a human and machine hybrid driving environment.

This book reports on cutting-edge theories and methods for analyzing complex systems, such as transportation and communication networks and discusses multi-disciplinary approaches to dependability problems encountered when dealing with complex systems in practice.

This is a volume consisting of selected papers that were presented at the 3rd St.

The present book develops the mathematical and numerical analysis of linear, elliptic and parabolic partial differential equations (PDEs) with coefficients whose logarithms are modelled as Gaussian random fields (GRFs), in polygonal and polyhedral physical domains.

Statistics is confusing, even for smart, technically competent people.

This is a collection of refereed papers presented at the 4th International Conference on Mathematical Population Dynamics.

This book constitutes the refereed proceedings of the 17th International Conference on Simulation of Adaptive Behavior, SAB 2024, held in Irvine, CA, USA, during September 9-12, 2024.

Learning with uncertainty covers a broad range of scenarios in machine learning, this book mainly focuses on: (1) Decision tree learning with uncertainty, (2) Clustering under uncertainty environment, (3) Active learning based on uncertainty criterion, and (4) Ensemble learning in a framework of uncertainty.

This book covers techniques that can be used to analyze data from IoT sensors and addresses questions regarding the performance of an IoT system.

This book examines Florence Nightingale David's life, contributions, and relationships throughout her life, as well as her subsequent legacy.

This is the first book dedicated to reviewing the mathematics of random tilings of large domains on the plane.

Although the web and online SAS(R) communities can provide volumes of information for programmers, these resources are often overwhelming and lack a simple path to guide coding SAS.

This book highlights the forefront of research on statistical distribution theory, with a focus on unconventional random quantities, and on phenomena such as random partitioning.

This book reports on cutting-edge theories and methods for analyzing complex systems, such as transportation and communication networks and discusses multi-disciplinary approaches to dependability problems encountered when dealing with complex systems in practice.

A greatly expanded and heavily revised second edition, this popular guide provides instructions and clear examples for running analyses of variance (ANOVA) and several other related statistical tests of significance with SPSS.

This book introduces undergraduate students of engineering and science to applied mathematics essential to the study of many problems.

A complete guide to cutting-edge techniques and best practices for applying covariance analysis methods The Second Edition of Analysis of Covariance and Alternatives sheds new light on its topic, offering in-depth discussions of underlying assumptions, comprehensive interpretations of results, and comparisons of distinct approaches.

This book expands on the classical statistical multivariate analysis theory by focusing on bilinear regression models, a class of models comprising the classical growth curve model and its extensions.