Probability and statistics

This book provides solid sociological foundations to computational social science (CSS).

Factor Analysis is a genetic term for a somewhat vaguely delimited set of techniques for data processing, mainly applicable to the social and biological sciences.

Mathematical models are used to convert real-life problems using mathematical concepts and language.

This book constructs input finite dimensional (FD) models that are amendable for numerical calculations and provides accurate representations for responses of dynamical systems to these inputs, i.

This book expands on the classical statistical multivariate analysis theory by focusing on bilinear regression models, a class of models comprising the classical growth curve model and its extensions.

This book focuses on methods and models in classification and data analysis and presents real-world applications at the interface with data science.

The subject of fractional calculus has gained considerable popularity and importance during the past three decades, mainly due to its validated applications in various fields of science and engineering.

R is a freely available, open-source statistical programming environment which provides powerful statistical analysis tools and graphics outputs.

Linear Models and the Relevant Distributions and Matrix Algebra: A Unified Approach, Volume 2 covers several important topics that were not included in the first volume.

Efficient transfer between science and society is crucial for their future development.

This second volume includes papers presented at the Eleventh World Congress of the Econometric Society, addressing topics such as big data, macroeconomics, financial markets, and partially identified models.

This textbook introduces readers to practical statisticalissues by presenting them within the context of real-life economics andbusiness situations.

This book gathers selected science and technology papers presented at the 2018 International Conference on Science Technology and Social Sciences (ICSTSS 2018), organised bi-annually by Universiti Teknologi MARA Pahang, Malaysia.

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

Until now there were no published analyses of the recent solvency work conducted in Europe, specifically the risk categories proposed by the International Actuarial Association (IAA).

This complete resource on the theory and applications of reliability engineering, probabilistic models and risk analysis consolidates all the latest research, presenting the most up-to-date developments in this field.

Helping students develop a good understanding of asymptotic theory, Introduction to Statistical Limit Theory provides a thorough yet accessible treatment of common modes of convergence and their related tools used in statistics.

The book provides a self-contained treatment of stochastic finite element methods.

This book provides a coherent description of foundational matters concerning statistical inference and shows how statistics can help us make inductive inferences about a broader context, based only on a limited dataset such as a random sample drawn from a larger population.

This book introduces key results essential for financial practitioners by means of concrete examples and a fully rigorous exposition.

At what level of physical existence does "e;quantum behavior"e; begin?

Statisticians have met the need to test hundreds or thousands of genomics hypotheses simultaneously with novel empirical Bayes methods that combine advantages of traditional Bayesian and frequentist statistics.

in die Statistik 5.

A beginner s guide to stochastic growth modeling The chief advantage of stochastic growth models over deterministic models is that they combine both deterministic and stochastic elements of dynamic behaviors, such as weather, natural disasters, market fluctuations, and epidemics.

Give Your Students the Proper Groundwork for Future Studies in OptimizationA First Course in Optimization is designed for a one-semester course in optimization taken by advanced undergraduate and beginning graduate students in the mathematical sciences and engineering.

The term "e;data"e; being mostly used, experimented, analyzed, and researched, "e;Data Science and its Applications"e; finds relevance in all domains of research studies including science, engineering, technology, management, mathematics, and many more in wide range of applications such as sentiment analysis, social medial analytics, signal processing, gene analysis, market analysis, healthcare, bioinformatics etc.

This two-volume set LNCS 12269 and LNCS 12270 constitutes the refereed proceedings of the 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020, held in Leiden, The Netherlands, in September 2020.

Based on the author's course at NYU, Linear Algebra and Probability for Computer Science Applications gives an introduction to two mathematical fields that are fundamental in many areas of computer science.

Wouldn't it be great if there were a statistics book that made histograms, probability distributions, and chi square analysis more enjoyable than going to the dentist?

This book focuses on the application of the partial hedging approach from modern math finance to equity-linked life insurance contracts.

Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure.

An accessible and engaging introduction to the study of probability and statistics Utilizing entertaining real-world examples, A Probability and Statistics Companion provides aunique, interesting, and accessible introduction to probability and statistics.

This book addresses the difficulties experienced by wet lab researchers with the statistical analysis of molecular biology related data.

The volume describes the main theoretical propositions of the methodology to predict mechanical reliability under conditions of repeated exposure to random extreme loads.

Establishing a new concept of local Lyapunov exponents the author brings together two separate theories, namely Lyapunov exponents and the theory of large deviations.

In cancer research, a traditional phase II trial is designed as a single-arm trial that compares the experimental therapy to a historical control.

This unique book develops the classical subjects of geometric probability and integral geometry.

Many methods for analyzing clustered data exist, all with advantages and limitations in particular applications.

This 1998 book is a lucid discussion of statistical and sampling techniques of relevance to field biologists.

Useful in many areas of medicine and biology, Bayesian methods are particularly attractive tools for the design of clinical trials and diagnostic tests, which are based on established information, usually from related previous studies.

Twenty-four million people wager nearly $3 billion on college basketball pools each year, but few are aware that winning strategies have been developed by researchers at Harvard, Yale, and other universities over the past two decades.

From the reviews of the First Edition: "e;This excellent book is based on several sets of lecture notes written over a decade and has its origin in a one-semester course given by the author at the ETH, Zurich, in the spring of 1970.

This first textbook on statistical machine translation shows students and developers how to build an automatic language translation system.

Using data from one season of NBA games, Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball.

Analysis of Correlated Data with SAS and R: 4th edition presents an applied treatment of recently developed statistical models and methods for the analysis of hierarchical binary, count and continuous response data.

Modern Statistical, Systems, and GPSS Simulation, Second Edition introduces the theory and implementation of discrete-event simulation.

Belief change is an emerging field of artificial intelligence and information science dedicated to the dynamics of information and the present book provides a state-of-the-art picture of its formal foundations.