Probability and statistics

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research, such as cancer.

Why study the theory of experiment design?

It has recently become apparent that "e;quality"e; is quickly becoming the single most important factor for success and growth in business.

The book presents a general mathematical framework able to detect and to characterise, from a morphological and statistical perspective, patterns hidden in spatial data.

This book explores the problem of minimal valid graph coloring, first in the form of games and then of resolution algorithms.

Lucidly Integrates Current ActivitiesFocusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other.

Building upon the technical and organizational groundwork presented in the first edition, Risk Assessment and Decision Making in Business and Industry: A Practical Guide, Second Edition addresses the many aspects of risk/uncertainty (R/U) process implementation.

Applied Multiple Regression/Correlation Analysis for Aviation Research describes and illustrates multiple regression/correlation (MRC) analysis in an aviation context, including flight instruction, airport design, airline routes, and aviation human factors research.

With today's consumers spending more time on their mobiles than on their PCs, new methods of empirical stochastic modeling have emerged that can provide marketers with detailed information about the products, content, and services their customers desire.

Handbook of Cluster Analysis provides a comprehensive and unified account of the main research developments in cluster analysis.

Showing how robust response surface methodology (RRSM) can overcome the limitations of RSM, this book presents RRS designs, along with the relevant regression and positive data analysis techniques.

This book describes how Bayesian methods work.

This book describes how Bayesian methods work.

Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics.

"e;Revised and updated edition of a standard in the field.

Since 1945, "e;The Annual Deming Conference on Applied Statistics"e; has been an important event in the statistics profession.

This book is a concise hands-on guide that provides students a useful tool for a systematic approach to scientific inquiry.

The book is a collection of different aspects of outliers and related topics written by experts.

This textbook provides the foundation for a course that takes PhD students in empirical accounting research from the very basics of statistics, data analysis, and causal inference up to the point at which they conduct their own research.

The new book highlights the application of artificial intelligence, machine learning, and deep learning techniques to diagnose medical problems.

This book provides an overview and compilation of contemporary topics and innovative approaches in biostatistical modeling through their applications to evidence-based public health research and decision-making.

This book provides an overview and compilation of contemporary topics and innovative approaches in biostatistical modeling through their applications to evidence-based public health research and decision-making.

Since most datasets contain a number of variables, multivariate methods are helpful in answering a variety of research questions.

This book explores the problem of minimal valid graph coloring, first in the form of games and then of resolution algorithms.

Although a useful and important tool, the potential of mathematical modelling for decision making is often neglected.

Learn How to Program Stochastic ModelsHighly recommended, the best-selling first edition of Introduction to Scientific Programming and Simulation Using R was lauded as an excellent, easy-to-read introduction with extensive examples and exercises.

The book discusses various inequalities and sharp bounds for the usual order statistics as well as some functions of them.

Keine ausführliche Beschreibung für "Mathematische Methoden der Zuverlässigkeitstheorie II" verfügbar.

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

The book discusses various inequalities and sharp bounds for the usual order statistics as well as some functions of them.

Large observational studies involving research questions that require the measurement of several features on each individual arise in many fields including the social and medical sciences.

While there have been few theoretical contributions on the Markov Chain Monte Carlo (MCMC) methods in the past decade, current understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds.

Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case.

Building on its best-selling predecessors, Basic Statistics and Pharmaceutical Statistical Applications, Third Edition covers statistical topics most relevant to those in the pharmaceutical industry and pharmacy practice.

This book provides a conceptual introduction to regression analysis and machine learning and their applications in education research.

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.

Keine ausführliche Beschreibung für "Geostatistik" verfügbar.

Retaining all the material from the second edition and adding substantial new material, this third edition presents models and statistical methods for analyzing spatially referenced point process data.

This book investigates the critical gain design in stochastic iterative learning control (SILC), including four specific gain design strategies: decreasing gain design, adaptive gain design, event-triggering gain design, and optimal gain design.

In today's healthcare landscape, there is a pressing need for quantitative methodologies that include the patients' perspective in any treatment decision.

This textbook, which is based on the second edition of a book that has been previously published in German language, provides a comprehension-oriented introduction to asymptotic stochastics.