Probability and statistics

This unified random matrix approach to large-dimensional machine learning covers applications from power detection to deep neural networks.

This greatly expanded third edition of Survival Analysis- A Self-learning Text provides a highly readable description of state-of-the-art methods of analysis of survival/event-history data.

Novel Techniques for Analyzing and Combining Data from Modern Biological StudiesBroadens the Traditional Definition of Meta-AnalysisWith the diversity of data and meta-data now available, there is increased interest in analyzing multiple studies beyond statistical approaches of formal meta-analysis.

This volume convenes carefully selected, peer-reviewed papers presented at the BIOMAT 2023 International Symposium, which was virtually held on November 6-9, 2023, with an organization staff based in Rio de Janeiro, Brazil.

Weareproudtopresenttheproceedingsoftheseventhbiennialconferenceinthe Intelligent Data Analysis series.

Statistical methods provide a logical, coherent framework in which data from experimental science can be analyzed.

This text describes a comprehensive adjoint sensitivity analysis methodology (nth-CASAM), developed by the author, which enablesthe efficient and exact computation of arbitrarily high-order functional derivatives of model responses to model parameters in large-scale systems.

Mit diesem Lehrbuch sollen Sie befähigt werden, sich die Grundlagen der Wirtschaftsstatistik anschaulich und zusammenhängend anzueignen.

Multivariate statistical analysis has undergone a rich and varied evolution during the latter half of the 20th century.

Graph Theory is a branch of discrete mathematics.

Categorical Statistics for CommunicationResearch presents scholars with a discipline-specific guide to categorical data analysis.

This handy reference book detailing the intricacies of R covers version 4.

This text develops the necessary background in probability theory underlying diverse treatments of stochastic processes and their wide-ranging applications.

This volume is based on the NATO Advanced Study Institute, "e;Advances in Mor- phometries"e; held in 11 Ciocco, Tuscany, Italy from July 18-30, 1993, and directed by Leslie F.

Praise for the First Edition "e; [t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics.

This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables.

The analysis prediction and interpolation of economic and other time series has a long history and many applications.

As in its first edition, the new edition of Quantitative Corpus Linguistics with R demonstrates how to process corpus-linguistic data with the open-source programming language and environment R.

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.

Sample Sizes for Clinical Trials, Second Edition is a practical book that assists researchers in their estimation of the sample size for clinical trials.

Confusing Textbooks?

The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others.

"e;This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist.

Mathematics and Statistics for Financial Risk Management is a practical guide to modern financial risk management for both practitioners and academics.

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.

Statistics is the language of modern empirical social and behavioural science and the varieties of regression form the basis of this language.

This book constitutes the proceedings of the 14th International Conference on Queueing Theory and Network Applications, QTNA 2019, held in Ghent, Belgium, in August 2019.

Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers.

Highlighting modern computational methods, Applied Stochastic Modelling, Second Edition provides students with the practical experience of scientific computing in applied statistics through a range of interesting real-world applications.

Although several books or monographs on multiobjective optimization under uncertainty have been published, there seems to be no book which starts with an introductory chapter of linear programming and is designed to incorporate both fuzziness and randomness into multiobjective programming in a unified way.

This volume gathers contributions from theoretical, experimental and computational researchers who are working on various topics in theoretical/computational/mathematical neuroscience.

Written in a lecture format with solved problems at the end of each chapter, this book surveys quantitative modeling and decision analysis techniques.

This book is a text at the introductory graduate level, for use in the one- semester or two-quarter probability course for first-year graduate students that seems ubiquitous in departments of statistics, biostatistics, mathemat- ical sciences, applied mathematics and mathematics.

This book provides empirical applications of macroeconometric methods through discussions on  key issues in the  Indian economy.

My goal in writing this book has been to provide teachers and students of multi- variate statistics with a unified treatment ofboth theoretical and practical aspects of this fascinating area.

The monograph text is based on lectures delivered by author during many years for students of Applied Iechanics Department of Bauman Ioscow State Technical University.

This text contains ample material for a one term precalculus introduction to probability theory.

Mendelian Randomization: Methods For Causal Inference Using Genetic Variants provides thorough coverage of the methods and practical elements of Mendelian randomization analysis.

With each new release of Stata, a comprehensive resource is needed to highlight the improvements as well as discuss the fundamentals of the software.

The European Workshop on White Dwarfs was initiated by Prof.

This volume is a collection of survey papers on recent developments in the fields of quasi-Monte Carlo methods and uniform random number generation.

The main subject of the book is stochastic analysis and its various applications to mathematical finance and statistics of random processes.

See How to Use Statistics for New Testament InterpretationThe Synoptic Problem and Statistics lays the foundations for a new area of interdisciplinary research that uses statistical techniques to investigate the synoptic problem in New Testament studies, which concerns the relationships between the Gospels of Matthew, Mark, and Luke.

Missing data arise in almost all scientific disciplines.

Statistical methods for sequential hypothesis testing and changepoint detection have applications across many fields, including quality control, biomedical engineering, communication networks, econometrics, image processing, security, etc.

Statistic: A Concise Mathematical Introduction for Students and Scientists offers a one academic term text that prepares the student to broaden their skills in statistics, probability and inference, prior to selecting their follow-on courses in their chosen fields, whether it be engineering, computer science, programming, data sciences, business or economics.

Das Buch bietet eine Einführung in Wahrscheinlichkeitsmodelle und die Schätzung solcher Modelle auf der Grundlage von Beobachtungen.

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.