Probability and statistics

Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling.

Non-analogue Monte Carlo methods are useful when the direct simulation techniques are insufficient.

This book provides an undergraduate-level introduction to discrete and continuous-time Markov chains and their applications, with a particular focus on the first step analysis technique and its applications to average hitting times and ruin probabilities.

This book discusses recent developments in mathematical programming and game theory, and the application of several mathematical models to problems in finance, games, economics and graph theory.

The objective of Kai Zhang and his research is to assess the existing process monitoring and fault detection (PM-FD) methods.

This book describes the basic concepts of risk and reliability with detailed descriptions of the different levels of probabilistic safety assessment of nuclear power plants (both internal and external).

Data Science: A First Introduction with Python focuses on using the Python programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference.

This book provides an overview of the application of statistical methods to problems in metrology, with emphasis on modelling measurement processes and quantifying their associated uncertainties.

This book offers a systematic and rigorous treatment of continuous-time Markov decision processes, covering both theory and possible applications to queueing systems, epidemiology, finance, and other fields.

The mathematical and statistical tools needed in the rapidly growing quantitative finance field With the rapid growth in quantitative finance, practitioners must achieve a high level of proficiency in math and statistics.

In the spatial or spatio-temporal context, specifying the correct covariance function is fundamental to obtain efficient predictions, and to understand the underlying physical process of interest.

This concise textbook, fashioned along the syllabus for master's and Ph.

Each year more and more scholars are becoming aware of the importance of the statistical study of literary texts.

A New Approach to Sound Statistical ReasoningInferential Models: Reasoning with Uncertainty introduces the authors' recently developed approach to inference: the inferential model (IM) framework.

Americans are bombarded with statistical data each and every day, and healthcare professionals are no exception.

Bayesian adaptive designs provide a critical approach to improve the efficiency and success of drug development that has been embraced by the US Food and Drug Administration (FDA).

Offering a unique balance between applications and calculations, Monte Carlo Methods and Models in Finance and Insurance incorporates the application background of finance and insurance with the theory and applications of Monte Carlo methods.

This book is devoted to the system analysis of statistical experiments, determined by the averaged sums of sampling random variables.

This book constitutes the proceedings of the 17th International Conference on Information Technologies and Mathematical Modelling, ITMM 2018, named after A.

This book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science students.

This book presents a unique collection of contributions on modern methods and applications in three key areas of statistics, celebrating the significant work of Wolfgang Schmid in this field.

This book summarizes the research of more than a decade.

Thanks to the omnipresent computer, current statistics can include data files of many thousands of values, and can perform any exploratory analysis in less than seconds.

30-Second Data Science covers 50 key elements in the field of data-gathering from basic to ethics, explained clearly and concisely.

In this book the authors provide a fresh look at basic reliability and maintainability engineering techniques and management tools for ap- plication to the system maintenance planning and implementation process.

During the of Fall 1991, The Centre de Recerca Matematica, a research institute sponsored by the Institut d'Estudis Catalans, devoted a quarter to the study of stochastic analysis.

Models for Non-Modelers focuses not on how to design models but on how to understand and critically appraise them.

The most important properties of normal and Student t-distributions are presented.

It has been 15 years since the first edition of Stochastic Integration and Differential Equations, A New Approach appeared, and in those years many other texts on the same subject have been published, often with connections to applications, especially mathematical finance.

Since the development of the first intelligence test in the early 20th century, educational and psychological tests have become important measurement techniques to quantify human behavior.

Design and Analysis of Cross-Over Trials is concerned with a specific kind of comparative trial known as the cross-over trial, in which subjects receive different sequences of treatments.

The seventh volume in the SemStat series, Statistical Methods for Stochastic Differential Equations presents current research trends and recent developments in statistical methods for stochastic differential equations.

This textbook summarizes the different statistical, scientific, and financial data analysis methods for users ranging from a high school level to a professional level.

This book developed from the need to teach a linear algebra course to students focused on data science and bioinformatics programs.

This volume of the Biostatistics and Health Sciences Set focuses on statistics applied to clinical research.

This book is divided into two parts, the first of which seeks to connect the phase transitions of various disciplines, including game theory, and to explore the synergies between statistical physics and combinatorics.

Introduction to Statistics for Forensic Scientists is an essential introduction to the subject, gently guiding the reader through the key statistical techniques used to evaluate various types of forensic evidence.

500 Ways to achieve your best score with this popular study guide for the AP Statistics Exam We've selected these 500 questions to help you study more effectively, use your preparation time wisely, and get your best results.

?

The aspects of this text which we believe are novel, at least in degree, include: an effort to motivate different sections with practical examples and an empirical orientation; an effort to intersperse several easily motivated examples throughout the book and to maintain some continuity in these examples; and the extensive use of Monte Carlo simulations to demonstrate particular aspects of the problems and estimators being considered.

Data graphics are used extensively to present information.