Probability and statistics

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 Second Bayesian Young Statisticians Meeting (BAYSM 2014) and the research presented here facilitate connections among researchers using Bayesian Statistics by providing a forum for the development and exchange of ideas.

This book constitutes the refereed proceedings of the Second International Workshop on Connectomics in NeuroImaging, CNI 2018, held in conjunction with MICCAI 2018 in Granada, Spain, in September 2018.

Concise, mathematically clear, and comprehensive treatment of the subject.

Praise for the Third Edition "e;It is, as far as I'm concerned, among the best books in math ever written.

This monograph deals with spatially dependent nonstationary time series in a way accessible to both time series econometricians wanting to understand spatial econometics, and spatial econometricians lacking a grounding in time series analysis.

Discover Novel and Insightful Knowledge from Data Represented as a GraphPractical Graph Mining with R presents a "e;do-it-yourself"e; approach to extracting interesting patterns from graph data.

This book presents contributions and review articles on the theory of copulas and their applications.

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.

A thoroughly revised and updated edition of this introduction to modern statistical methods for shape analysis Shape analysis is an important tool in the many disciplines where objects are compared using geometrical features.

Stealth technology is a crucial pre-requisite in the combat zone, where swiftness, surprise and initiative are the decisive elements for survivability.

Maintaining the same accessible and hands-on presentation, Introductory Biostatistics, Second Edition continues to provide an organized introduction to basic statistical concepts commonly applied in research across the health sciences.

This book provides state-of-art statistical methodologies, practical considerations from regulators and sponsors, logistics, and real use cases for practitioners for the uptake of RWE/D.

At the heart of the optimization domain are mathematical modeling of the problem and the solution methodologies.

This proceedings volume highlights the latest research and developments in psychometrics and statistics.

There exist literary histories of probability and scientific histories of probability, but it has generally been thought that the two did not meet.

Mathematical Engineering of Deep Learning provides a complete and concise overview of deep learning using the language of mathematics.

Cluster analysis comprises a range of methods for classifying multivariate data into subgroups.

Multivariate Statistical Inference is a 10-chapter text that covers the theoretical and applied aspects of multivariate analysis, specifically the multivariate normal distribution using the invariance approach.

Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning conceptsKey FeaturesExplore industry-tested machine learning techniques used to forecast millions of time seriesGet started with the revolutionary paradigm of global forecasting modelsGet to grips with new concepts by applying them to real-world datasets of energy forecastingBook DescriptionWe live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting.

Practical in its approach, Applied Bayesian Forecasting and Time Series Analysis provides the theories, methods, and tools necessary for forecasting and the analysis of time series.

Finance students and practitioners may ask: can machines learn everything?

This second edition of Lessons in Play reorganizes the presentation of the popular original text in combinatorial game theory to make it even more widely accessible.

There is a dearth of relevant books dealing with both theory and application of time series analysis techniques, particularly in the field of water resources engineering.

Power and Sample Size in R guides the reader through power and sample size calculations for a wide variety of study outcomes and designs and illustrates their implementation in R software.

This book will provide a comprehensive overview on human action analysis with randomized trees.

A concise, easily accessible introduction to descriptive and inferential techniques Statistical Inference: A Short Course offers a concise presentation of the essentials of basic statistics for readers seeking to acquire a working knowledge of statistical concepts, measures, and procedures.

With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape.

 Deep Learning with R introduces deep learning and neural networks using the R programming language.

Our basic question is: Given a collection of DNA sequences, what underlying forces are responsible for the observed patterns of variability?

Praise for the First Edition "e; .

Numerical Methods in Finance have recently emerged as a new discipline at the intersection of probability theory, finance and numerical analysis.

This book studies values and attitudes in the Gulf region.

The Monte Carlo method is inherently parallel and the extensive and rapid development in parallel computers, computational clusters and grids has resulted in renewed and increasing interest in this method.

This thesis presents the application of non-perturbative, or functional, renormalization group to study the physics of critical stationary states in systems out-of-equilibrium.

Modern survival analysis and more general event history analysis may be effectively handled in the mathematical framework of counting processes, stochastic integration, martingale central limit theory and product integration.

With this book, which is based on the third edition of a book first written in German about random walks, the author succeeds in a remarkably playful manner in captivating the reader with numerous surprising random phenomena and non-standard limit theorems related to simple random walks and related topics.

The subject of this book is applied Bayesian methods for chemistry, manufacturing, and control (CMC) studies in the biopharmaceutical industry.

This book shows you how to analyze data sets systematically and to use the R Commander to extract information from data almost effortlessly.

A comprehensive guide to statistical hypothesis testing with examples in SAS and R When analyzing datasets the following questions often arise: Is there a short hand procedure for a statistical test available in SAS or R?

Mathematics of Chance utilizes simple, real-world problems-some of which have only recently been solved-to explain fundamental probability theorems, methods, and statistical reasoning.