Probability and statistics

This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications.

to Stochastic Calculus for Finance A New Didactic Approach With 6 Figures 123 Prof.

By studying the ability of the Normal Tempered Stable (NTS) model to fit thestatistical features of intraday data at a 5 min sampling frequency, Florian Jacobs extends the research on high frequency data as well as the appliance of tempered stable models.

This book collects papers presented at the International Conference on Mathematical Modelling and Computational Intelligence Techniques (ICMMCIT) 2021, held at the Department of Mathematics, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Tamil Nadu, India, from 10-12 February 2021.

This book covers the application of algebraic inequalities for reliability improvement and for uncertainty and risk reduction.

Taking readers with a basic knowledge of probability and real analysis to the frontiers of a very active research discipline, this textbook provides all the necessary background from functional analysis and the theory of PDEs.

Calculus for Business and Economics: An Example-Based Introduction is designed for first-year university students specializing in business and economics.

Measure, Integral and Probability is a gentle introduction that makes measure and integration theory accessible to the average third-year undergraduate student.

This monograph, now in a thoroughly revised second edition, develops the theory of stochastic calculus in Hilbert spaces and applies the results to the study of generalized solutions of stochastic parabolic equations.

An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical.

All over the world a growing number of people are viewing trading the markets as a valuable source of additional income or even as a new career option.

This richly illustrated book provides an easy-to-read introduction to the challenges of organizing and integrating modern data worlds, explaining the contribution of public statistics and the ISO standard SDMX (Statistical Data and Metadata Exchange).

This book introduces Probability Theory with R software and explains abstract concepts in a simple and easy-to-understand way by combining theory and computation.

R for Political Data Science: A Practical Guide is a handbook for political scientists new to R who want to learn the most useful and common ways to interpret and analyze political data.

System Modeling and Optimization is an indispensable reference for anyone interested in the recent advances in these two disciplines.

This volume is a summary of material presented in the course given in the International School of Phannacology on "e;Drug Epidemiology and Post-Marketing Surveillance"e; between September 27 and October 8, 1990, at the "e;Ettore Majorana Center for Scientific Culture"e; in Erice, Sicily.

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This text brings together important ideas on the model-based approach to sample survey, which has been developed over the last twenty years.

Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data.

This book serves as an introductory text in mathematical programming and optimization for students having a mathematical background that includes one semester of linear algebra and a complete calculus sequence.

Logic and Complexity looks at basic logic as it is used in Computer Science, and provides students with a logical approach to Complexity theory.

Over the course of his distinguished career, Robert Strichartz (1943-2021) had a substantial impact on the field of analysis with his deep, original results in classical harmonic, functional, and spectral analysis, and in the newly developed analysis on fractals.

Mathematicians Playing Games explores a wide variety of popular mathematical games, including their historical beginnings and the mathematical theories that underpin them.

This book provides a comprehensive introduction to statistical approaches for the assessment of complex environmental exposures, such as pollutants and chemical mixtures, within the exposome framework.

The cooperation and contamination between mathematicians, statisticians and econometricians working in actuarial sciences and finance is improving the research on these topics and producing numerous meaningful scientific results.

This book presents the state-of-the-art methodology and detailed analytical models and methods used to assess the reliability of complex systems and related applications in statistical reliability engineering.

A user-oriented, practical introduction outlining methods for analysing actuarial and financial risk.

Integrating interesting and widely used concepts of financial engineering into traditional statistics courses, Introduction to Probability and Statistics for Science, Engineering, and Finance illustrates the role and scope of statistics and probability in various fields.

Linear Models and the Relevant Distributions and Matrix Algebra provides in-depth and detailed coverage of the use of linear statistical models as a basis for parametric and predictive inference.

Cloud computing offers subscription-based on-demand services, and it has emerged as the backbone of the computing industry.

This book formulates methods for modeling continuous and categorical correlated outcomes that extend the commonly used methods: generalized estimating equations (GEE) and linear mixed modeling.

This book contains a series of original contributions in the area of Stochastic Dynamics, which demonstrates the impact of Mike Lin's research and teaching in the area of random vibration and structural dynamics.

From the Foreword:"e;Big Data Management and Processing is [a] state-of-the-art book that deals with a wide range of topical themes in the field of Big Data.

This book, published in honor of Professor Laurent Praly on the occasion of his 65th birthday, explores the responses of some leading international authorities to new challenges in nonlinear and adaptive control.

Bayesian Networks in R with Applications in Systems Biology is unique as it introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R.

Explores mathematical statistics in its entirety from the fundamentals to modern methods This book introduces readers to point estimation, confidence intervals, and statistical tests.

The fourth edition of this widely used textbook offers a new perspective.

This book offers a comprehensive and accessible exposition of Euclidean Distance Matrices (EDMs) and rigidity theory of bar-and-joint frameworks.

This book illustrates numerous statistical practices that are commonly used by medical researchers, but which have severe flaws that may not be obvious.

Fatigue of engineering materials is a very complicated process that is difficult to accurately describe and predict.

Medicine deals with treatments that work often but not always, so treatment success must be based on probability.

The book introduces to the reader a number of cutting edge statistical methods which can e used for the analysis of genomic, proteomic and metabolomic data sets.

This book focuses on two-time-scale Markov chains in discrete time.

This monograph examines the domain of classical political economy using the methodologies developed in recent years both by the new discipline of econo-physics and by computing science.

Praise for the Second Edition:"e;.

This book reviews the latest risk-based techniques to protect national interests from invasive pests and pathogens before, at and within national borders.

This book constitutes the refereed proceedings of the 15th International Scientific Conference on Information Technologies and Mathematical Modeling, named after A.

This guide offers a clear step-by-step approach for graduate students and early-career researchers, especially non-native English speakers, seeking to publish in international journals in the social sciences.

An insider account that encapsulates thirty years of experience in conducting data science in a political context.