Probability and statistics

An Introduction to Machine Learning in Finance, With Mathematical Background, Data Visualization, and R Nonparametric function estimation is an important part of machine learning, which is becoming increasingly important in quantitative finance.

Die statistische Versuchsplanung (Design of Experiment, DoE) ist ein Verfahren zur Analyse von (technischen) Systemen.

Epidemic trend analysis, timeline progression, prediction, and recommendation are critical for initiating effective public health control strategies, and AI and data analytics play an important role in epidemiology, diagnostic, and clinical fronts.

This book serves as a reference text for regulatory, industry and academic statisticians and also a handy manual for entry level Statisticians.

This new edition covers some of the key topics relating to the latest version of MS Office through Excel 2019, including the creation of custom ribbons by injecting XML code into Excel Workbooks and how to link Excel VBA macros to customize ribbon objects.

The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data.

In this book, the basic notions and tools of unimodality as they relate to probability and statistics are presented.

This book is devoted to biased sampling problems (also called choice-based sampling in Econometrics parlance) and over-identified parameter estimation problems.

This textbook presents an overview of the field of demography from a contemporary perspective.

***Winner of the 2008 Ziegel Prize for outstanding new book of the year*** Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account.

This book is an evolution from my book A First Course in Information Theory published in 2002 when network coding was still at its infancy.

This book provides a deep understanding of the relationship between machine learning and causal inference.

This book grew out of a course taught in the Department of Mathematics, Indian Institute of Technology, Delhi, which was tailored to the needs of the applied community of mathematicians, engineers, physicists etc.

This book provides an introduction to the use of statistical concepts and methods to model and analyze financial data.

Robustness of Statistical Tests provides a general, systematic finite sample theory of the robustness of tests and covers the application of this theory to some important testing problems commonly considered under normality.

Coefficient of Variation (CV) is a unit free index indicating the consistency of the data associated with a real-world process and is simple to mold into computational paradigms.

This book contains the latest advances in variational analysis and set / vector optimization, including uncertain optimization, optimal control and bilevel optimization.

A team of ASQ Fellows has created this study guide with new questions predominantly based on the best-selling third edition of The Certified Six Sigma Green Belt Handbook.

This textbook is the result of the enhancement of several courses on non-equilibrium statistics, stochastic processes, stochastic differential equations, anomalous diffusion and disorder.

This volume consists of twenty-four papers selected by the editors from the sixty-one papers presented at the 1st International Conference on Mathemati- cal Methods in Reliability held at the Politehnica University of Bucharest from 16 to 19 September 1997.

This book discusses globalization and its impact on human health.

This  comprehensive book, rich with applications,  offers a quantitative framework for the analysis of the various capture-recapture models for open animal populations, while also addressing associated computational methods.

Modern statistical software provides the ability to compute statistics in a timely, orderly fashion.

This volume is a collection of articles on reliability systems and Bayesian reliability analysis.

Geographic information, spatial analysis and geospatial technologies play an important role in understanding changes in planetary health and in defining the drivers contributing to different health outcomes both locally and globally.

This is the second volume of a text on the theory and practice of maximum penalized likelihood estimation.

Ruled varieties are unions of a family of linear spaces.

An accessible introduction to probability, stochastic processes, and statistics for computer science and engineering applicationsSecond edition now also available in Paperback.

Presents the classical theory of convergence of semigroups and looks at how it applies to real-world phenomena.

Emerging technologies generate data sets of increased size and complexity that require new or updated statistical inferential methods and scalable, reproducible software.

Take your machine learning skills to the next level by mastering the best framework for uncertainty quantification - Conformal PredictionKey FeaturesMaster Conformal Prediction, a fast-growing ML framework, with Python applications.

Building Science Graphics: An illustrated guide to communicating science through diagrams and visualizations is a practical guide for anyone-regardless of previous design experience and preferred drawing tools-interested in creating science-centric illustrated explanatory diagrams.

The change-point problem has attracted many statistical researchers and practitioners during the last few decades.

Meta-analysis is a powerful statistical methodology for synthesizing research evidence across independent studies.

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From Markov Jump Processes to Spatial Queues aims to develop a unified theory of spatial queues that yields concrete results for the performance analysis of mobile communication networks.

This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers.

We live in a world that is not quite "e;right.

It is a distinct pleasure to be invited to prepare a short Foreword to Biomedical Research: How to plan, publish and present it, by William F.

This book constitutes revised selected papers of the 20th International Conference on Information Technologies and Mathematical Modelling, ITMM 2021, named after A.

Data Analysis for Scientists and Engineers is a modern, graduate-level text on data analysis techniques for physical science and engineering students as well as working scientists and engineers.

This book offers a practical presentation of stochastic partial differential equations arising in physical applications and their numerical approximation.

Cancer is a dreaded disease.

Advanced Statistics with Applications in R fills the gap between several excellent theoretical statistics textbooks and many applied statistics books where teaching reduces to using existing packages.

If you know how to program with Python and also know a little about probability, youre ready to tackle Bayesian statistics.