Probability and statistics

In the dynamic landscape of modern data analysis, this curated guide by global experts explores the latest in statistical methodologies, modeling techniques, and optimization strategies.

A strong grasp of elementary statistics and probability, along with basic skills in using R, is essential for various scientific disciplines reliant on data analysis.

Adaptive filtering still receives attention in engineering as the use of the adaptive filter provides improved performance over the use of a fixed filter under the time-varying and unknown statistics environments.

This book is a step-by-step data story for analyzing ordinal data from start to finish.

Praise for the first edition:"e;This book would be especially good for the shelf of anyone who already knows nonparametrics, but wants a reference for how to apply those techniques in R.

Causal Inference in Pharmaceutical Statistics introduces the basic concepts and fundamental methods of causal inference relevant to pharmaceutical statistics.

The goal of this unique text is to provide an "e;experience"e; that would facilitate a better transition for mathematics majors to the advanced proof-based courses required for their major.

Praise for the first edition:"e;This book would be especially good for the shelf of anyone who already knows nonparametrics, but wants a reference for how to apply those techniques in R.

Make data-driven, informed decisions and enhance your statistical expertise in Python by turning raw data into meaningful insightsPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesGain expertise in identifying and modeling patterns that generate successExplore the concepts with Python using important libraries such as stats modelsLearn how to build models on real-world data sets and find solutions to practical challengesBook DescriptionThe ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights.

This book comprises the select peer-reviewed proceedings of the International Conference on Hydro and Renewable Energy (ICHRE 2022).

An applied introduction to probability and statistics, now in a practical, digital eBook format.

Introduction to Nuclear Engineering serves as an accompanying study guide for a complete, introductory single-semester course in nuclear engineering.

This new edition presents the essential topics in probability and statistics from a rigorous standpoint.

This book tackles the question of why the brain is so difficult to fully understand.

This book provides a foundation in stochastic, linear, and mixed-integer programming algorithms with a focus on practical computer algorithm implementation.

Unsupervised domain adaptation (UDA) is a challenging problem in machine learning where the model is trained on a source domain with labeled data and tested on a target domain with unlabeled data.

This new edition presents the essential topics in probability and statistics from a rigorous standpoint.

A strong grasp of elementary statistics and probability, along with basic skills in using R, is essential for various scientific disciplines reliant on data analysis.

An Introductory Handbook of Bayesian Thinking brings Bayesian thinking and methods to a wide audience beyond the mathematical sciences.

The theory of the measure of noncompactness has proved its significance in various contexts, particularly in the study of fixed point theory, differential equations, functional equations, integral and integrodifferential equations, optimization, and others.

An Introductory Handbook of Bayesian Thinking brings Bayesian thinking and methods to a wide audience beyond the mathematical sciences.

In an increasingly data-centric world, we all need to know how to read and interpret statistics.

The theory of the measure of noncompactness has proved its significance in various contexts, particularly in the study of fixed point theory, differential equations, functional equations, integral and integrodifferential equations, optimization, and others.

This book provides a foundation in stochastic, linear, and mixed-integer programming algorithms with a focus on practical computer algorithm implementation.

This book tackles the question of why the brain is so difficult to fully understand.

Learning a computer language like R can be either frustrating, fun or boring.

In an increasingly data-centric world, we all need to know how to read and interpret statistics.

Introduction to Statistical Analysis of Laboratory Data presents a detailed discussion of important statistical concepts and methods of data presentation and analysis Provides detailed discussions on statistical applications including a comprehensive package of statistical tools that are specific to the laboratory experiment process Introduces terminology used in many applications such as the interpretation of assay design and validation as well as fit for purpose procedures including real world examples Includes a rigorous review of statistical quality control procedures in laboratory methodologies and influences on capabilities Presents methodologies used in the areas such as method comparison procedures, limit and bias detection, outlier analysis and detecting sources of variation Analysis of robustness and ruggedness including multivariate influences on response are introduced to account for controllable/uncontrollable laboratory conditions

This book introduces all common errors in medical science fields and the ways to control them.

The ability to analyze and interpret enormous amounts of data has become a prerequisite for success in allied healthcare and the health sciences.

This textbook provides a unique instructional resource in experimental Physical Chemistry with case studies based on data taken from the scientific literature.

This book is a concise introduction to the stochastic calculus of variations for processes with jumps.

This book is a useful overview of results in multivariate probability distributions and multivariate analysis as well as a reference to harmonic analysis on symmetric cones adapted to the needs of researchers in analysis and probability theory.

Financial market modeling is a prime example of a real-life application of probability theory and stochastics.

Financial market modeling is a prime example of a real-life application of probability theory and stochastics.

Twisted Logic: Puzzles, Paradoxes, and Big Questions delves into the intriguing world of twisted logic, where everyday conundrums, bewildering paradoxes, and life's big questions are investigated and decoded.

This book presents the first comprehensive and modern mathematical treatment of these mean field particle models, including refined convergence analysis on nonlinear Markov chain models.

Introduction to Statistical Analysis of Laboratory Data presents a detailed discussion of important statistical concepts and methods of data presentation and analysis Provides detailed discussions on statistical applications including a comprehensive package of statistical tools that are specific to the laboratory experiment process Introduces terminology used in many applications such as the interpretation of assay design and validation as well as fit for purpose procedures including real world examples Includes a rigorous review of statistical quality control procedures in laboratory methodologies and influences on capabilities Presents methodologies used in the areas such as method comparison procedures, limit and bias detection, outlier analysis and detecting sources of variation Analysis of robustness and ruggedness including multivariate influences on response are introduced to account for controllable/uncontrollable laboratory conditions

R for Health Technology Assessment discusses the use of proper statistical software, specifically R, to perform the whole pipeline of analytic modelling in health technology assessment (HTA).

This book offers a thorough understanding of Hierarchical Archimedean Copulas (HACs) and their practical applications.

In their bestselling MATHEMATICAL STATISTICS WITH APPLICATIONS, premiere authors Dennis Wackerly, William Mendenhall, and Richard L.

Ordered Regression Models: Parallel, Partial, and Non-Parallel Alternatives presents regression models for ordinal outcomes, which are variables that have ordered categories but unknown spacing between the categories.

Dieses Lehrbuch liefert einen verständnisorientierten Einstieg in die asymptotische Stochastik.

Research Software Engineering: A Guide to the Open Source Ecosystem strives to give a big-picture overview and an understanding of the opportunities of programming as an approach to analytics and statistics.