Über uns

À propos de nous

Open Access

Karriere

Carrière

Aboverträge Kündigen

Résiliation d’abonnement

Vertrag widerrufen

Résiliation de contrat 

Datenschutz

Protection des données

Impressum

Mentions légales

Barrierefreiheit

Accessibilité

Zahlung und Lieferung

Paiement et livraison

Libra

Was ist Libra?

C'est quoi Libra ?

Kostenlos testen

Tester gratuitement

LEXIA

LEXIA Arbeitsrecht

Was ist LEXIA?

C'est quoi LEXIA ?

LEXIA Familienrecht

Zeitschriften

Revues

recht

Pflegerecht

ZBJV

forumpoenale

ZStrR

Anwaltsrevue

Revue de l'avocat

RSAS

FamPra.ch

ASYL

Life Science Recht

BlAR

Sachbuch

Livres spécialisés

LOKI

Semaphor

Fokusthemen

Focus

Thema

Recht studieren

Arbeitsrecht

Droit du travail

Studium

Études

Familienrecht

Droit de la famille

Sozialversicherungsrecht

Droit des assurances sociales

Strafrecht

Droit pénal

Aktienrecht

Droit des sociétés anonymes

Persönlichkeitsrecht

Droit de la personnalité

Publikationen

Publications

Literatur

Digitales Angebot

Bücher

Livres

Buchreihen

Collections

Onlinemodule LEXIA

Modules en ligne LEXIA

recht.ch

Studienliteratur

Littérature pour les études

Swisslex

Dienstleistungen

Services

Dienstleistungen für

Corporate Books

Publishing Lösungen

Aktuelles

Geschäftskunden

Professionnels

Corporate Publishing

Content solutions

Buchhandlungen

Librairies

Veranstaltungen

Événements

Presse

Dozierende

Enseignants

Studierende

Étudiants

Inserierende

Annonceurs

Newsletter

Blog

Autorinnen/Autoren

Auteurs

Unsere Schreibenden

Hilfe

Unsere Autorinnen und Autoren

Liste

Autorenlounge

Espace des auteurs

Bei uns publizieren

Publier chez nous

Open Access Policy

Verlag

Éditions

Themen

Mehr wissen

Mitarbeitende

Collaborateurs

Geschichte

Histoire

Unser Magazin

Notre magazine

Vision und Mission

Vision et mission

Partner

Partenaires

Unternehmenskultur

Culture d'entreprise

Kontakt

Contact

Shop

Schweizer Recht

Ausländisches Recht

Über unseren Shop

Neuerscheinungen

Nouveautés

Sortiment & Suche

Catalogue & Recherche

Vorankündigungen

À paraître

Nach Rechtsgebiet

Par domaine juridique

Kommentare

Commentaires

Gesamtkatalog

Catalogue complet

Fachkatalog Recht

Catalogue de droit

Starnes D: The Practice of Statistics
for the AP® Course

The Practice of Statistics for the AP® Course

Advancements in Intelligent and Sustainable Technologies and Systems

This handbook addresses how smart operations, management, and healthcare can be used to detect and analyze supply chain problems, business problems, and diseases. It also discusses the underlying methodologies and related security concerns.Handbook of Disruptive Technologies: Operations, Business, Management, and Healthcare provides updated and timely insights, unique approaches or frameworks, practical applications, and case studies. It provides in-depth knowledge of disruptive technologies and applies data analytics to different areas to solve complex situations. The handbook uses data analytics in decision-making and policy framing and identifies practices and applications of disruptive technologies that can assist organizations in gaining a competitive advantage. Valuable insights, innovative approaches, practical applications, and case studies can be found in this handbook as it effectively consolidates and synthesizes information to provide a comprehensive understanding of the subject matter.This book is tailored for researchers, academics, and professionals seeking to deepen their knowledge in the field.

Operations, Business, Management, and Healthcare

Handbook of Disruptive Technologies

The Investor's Field Guide

Independently published

The Investor's Field Guide - Forged in the Field Math Primer: Essentials Investment models rest on a mathematical foundation. This guide maps that foundation-probability, statistics, linear algebra, and stochastic calculus. With a word on Artificial Intelligence and Quantum. Focused on how these tools are built into the models and methods practitioners use daily. What it is, what it does, where it breaks down, and where to go when you need a deeper dive. A launching pad for the serious student. A sharp and reliable refresher for the busy practitioner. The Elements of Style got writers to the point. This gets you to the math.

Math Primer

Operations Research Series

This new edition textbook continues down the path that the first edition, winner of the 2013 IISE/Joint Publishers Book-of-the-Year Award, successfully carved out. The textbook targets engineering students and emphasizes the use of operations research models and solution methods important in the design, control, operation, and management of global supply chains.Completely updated, Supply Chain Engineering: Models and Applications, Second Edition stresses quantitative models and methods, highlights global supplier selection and vendor risk management techniques, and discusses the use of multiple criteria decision-making models in supply chain management. The new edition includes chapters on health and humanitarian supply chains, including disaster management and logistics modeling, and on warehousing and distribution. Disruptions to global supply chains due to the COVID-19 pandemic are discussed throughout the book. Industry and government strategies to make the global supply chains resilient are also presented. Thirty four case studies have been included to illustrate various supply chain models and methods. Exercises are included at the end of each chapter, and a solutions manual and PowerPoint slides are available for qualified textbook adoptions.The new edition continues to target upper-level undergraduate and graduate students in engineering, as well as MBA students in operations management, logistics, and supply chain management programs that emphasize quantitative analysis. It is also useful as a reference for technical professionals and researchers in industrial engineering, supply chain management, procurement, logistics and health administration.

Models and Applications

Supply Chain Engineering

Statistical Design and Inference for the Social Sciences&nbsp;goes beyond the math to teach students how to use data to support meaningful, causal arguments.

Statistical Design and Inference for the Social Sciences

Bayesian statistics and statistical practice have evolved over the years, driven by advancements in theory, methods, and computational tools. Bayesian Workflow explores the intricate workflows of applied Bayesian statistics, aiming to uncover the tacit knowledge often overlooked in published papers and textbooks. By systematizing the process of Bayesian model development, the book seeks to improve applied analyses and inspire future innovations in theory, methods, and software. It emphasizes the importance of iterative model building, model checking, computational troubleshooting, and simulated-data experimentation, offering a comprehensive perspective on statistical analysis.Through detailed examples and practical guidance, the book bridges the gap between theory and application, empowering practitioners and researchers to navigate the complexities of Bayesian inference. It is not a checklist or cookbook but a flexible framework for understanding and resolving challenges in statistical modeling and decision-making under uncertainty.Features<ul> <li>Covers all aspects of Bayesian statistical workflow, including model building, inference, validation, troubleshooting, and understanding</li> <li>Demonstrates iterative model development and computational problem-solving through real-world case studies</li> <li>Explores computational challenges, calibration checking, and connections between modeling and computation</li> <li>Highlights the importance of checking models under diverse conditions to understand their limitations and improve their robustness</li> <li>Discusses how Bayesian principles apply to non-Bayesian methods in statistics and machine learning</li> <li>Includes code snippets, exercises, and links to full datasets and code in R and Stan, with applicability to other programming environments like Python and Julia</li> </ul>This book is designed for practitioners of applied Bayesian statistics, particularly users of probabilistic programming languages such as Stan, as well as developers of methods and software tailored to these users. It also targets researchers in Bayesian theory and methods, offering insights into understudied aspects of statistical workflows. Instructors and students will find adaptable exercises and case studies to enhance their learning experience. Beyond Bayesian inference, the book's principles are relevant to users of non-Bayesian methods, making it a valuable resource for statisticians, data scientists, and machine learning professionals seeking to improve their modeling and decision-making processes.

Bayesian Workflow

Routledge Revivals

First published in 1972, in Descriptive Statistics and Probability Theory the numerical work- the selection of numerical data-is used as a basis for developing the statistical theory. Other starting points could have been chosen but a beginner will readily understand this sort of work and hence be able to 'get the feel of' statistics, untroubled by the learning of fresh concepts.From this numerical work emerges the 'need' to introduce counting techniques, permutations, combinations and the binomial theorem. In a similar way the necessity for probability is shown and the basic ideas are developed intuitively. A final short chapter shows how these ideas can be formalised within an axiomatic system. In all cases, simple intuitive ideas are taken as a starting point and then discussion leads to the final formalization. As concepts are introduced the language of statistics is developed. A large number of examples is used and they are all worked in full detail. Occasionally the reader is placed in a problem situation and invited to attempt his own individual solution before reading on to match his own attempts to other solutions. It is hoped that motivation of this sort will lead the reader to develop greater sensitivity than would otherwise be the case. This is an important read for students of statistics, mathematics and economics.

Descriptive Statistics and Probability Theory

Routledge-Giappichelli Studies in Business and Management

From Basic Growth Models to Computational Methods

Dynamic Programming for Economists

Prepare to view the world through a new lens - SPEED - a groundbreaking exploration that challenges your perceptions of life's driving force, from the internationally bestselling author'There is perhaps no other academic who paints pictures with numbers like Smil' GuardianIn a world that feels like it's moving faster than ever, Smil examines how our relentless pursuit of speed - in areas such as production, travel and communication - shapes not only our technological landscape but also our social and environmental realities. And through engaging anecdotes and striking statistics, Smil challenges the assumption that faster is always better.He invites readers to reconsider the implications of our collective obsession with speed, and its intricate relationship with nature and innovation. For example, he highlights the surprising fact that erosion can actually accelerate mountain growth, and he points out that the rapid adoption of mobile phones - achieving 90 per cent penetration in just nineteen years - reflects historical patterns of technological adoption, suggesting that our world may not be moving significantly faster than in the past.Whether exploring the tiny mites that can traverse 300 times their body length in a second or the societal impacts of high-frequency trading, Speed invites readers to engage in a more balanced conversation about the role of speed in our lives, in an age defined by haste.

How it Explains the World

Speed

New Machine Learning Methods for the Sustainable Development Goals

Pelican Books

'Probably the UK's greatest living statistician' TelegraphFrom the UK's 'statistical national treasure', a clever and data-driven guide to how we can live with risk and uncertaintyWe live in a world where uncertainty is inevitable. How should we deal with what we don't know? And what role do chance, luck and coincidence play in our lives?David Spiegelhalter has spent his career dissecting data in order to understand risks and assess the chances of what might happen in the future. In The Art of Uncertainty, he gives readers a window onto how we can all do this better.In engaging, crystal-clear prose, he takes us through the principles of probability, showing how it can help us think more analytically about everything from medical advice to pandemics and climate change forecasts, and explores how we can update our beliefs about the future in the face of constantly changing experience. Along the way, he explains why roughly 40% of football results come down to luck rather than talent, how the National Risk Register assesses near-term risks to the United Kingdom, and why we can be so confident that two properly shuffled packs of cards have never, ever been in the exact same order.Drawing on a wide range of captivating real-world examples, this is an essential guide to navigating uncertainty while also having the humility to admit what we do not know

How to Navigate Chance, Ignorance, Risk and Luck

The Art of Uncertainty

A novel framework for how entrepreneurs can use Bayesian reasoning to make decisions. This edited volume introduces and explores the concept of Bayesian entrepreneurship, a novel framework for understanding entrepreneurial decision-making under uncertainty. It brings together contributions from leading scholars to examine how entrepreneurs form beliefs about opportunities, learn through experimentation, and make strategic decisions. The Bayesian approach emphasizes the critical interaction between an entrepreneur’s prior beliefs and their capacity for learning and experimentation to update those beliefs. This dynamic process allows entrepreneurs to formulate and test theories about value creation and capture, attract resources and capabilities, and implement effective entrepreneurial strategies. The volume is organized around four key themes: <ul><li>the foundations and distinguishing features of Bayesian entrepreneurship </li><li>how entrepreneurs form and update prior beliefs in entrepreneurial decision-making</li><li>the role of experimentation and learning in entrepreneurial strategy</li><li>applications to entrepreneurship education and practice</li></ul> Bayesian Entrepreneurship offers both theoretical contributions advancing our understanding of entrepreneurial decision-making and practical insights for entrepreneurs and educators.

Bayesian Entrepreneurship

Chapman and Hall/CRC Series on Statistics in Business and Economics

Introduction to Bayesian Econometrics: A GUIded Toolkit Using R offers a practical, conceptually clear, and computationally accessible pathway into Bayesian data analysis. Designed for readers who wish to apply Bayesian methods without necessarily investing years in programming, the book combines rigorous treatment of foundational ideas with a graphical user interface (GUI) that allows users to run Bayesian regression models in a user-friendly environment.The first part develops the mathematical foundations of Bayesian inference by presenting all derivations step-by-step. This transparent treatment of conjugate models, including posterior analysis, marginal likelihoods, and posterior predictive distributions, provides readers with a strong theoretical base for the more advanced material that follows.The second part focuses on implementation. It introduces the custom GUI for readers with little or no programming experience, demonstrates how to fit Bayesian models using established R packages, and guides more advanced users through programming key components of Bayesian samplers from scratch. This integrated approach enables readers with different backgrounds to engage with Bayesian methods at their preferred level of computational depth.The third part extends the framework to modern Bayesian econometrics. It covers Bayesian machine learning, causal inference, and approximate methods, illustrating how Bayesian ideas can be applied to contemporary empirical challenges. By combining theory, software, and hands-on computation, the book provides a comprehensive entry point into both classical and modern Bayesian analysis.Across all parts, the book is designed to support a wide range of users -beginners, intermediate programmers, and advanced learners-. To the best of the author's knowledge, no existing text combines mathematical transparency, software accessibility, and modern Bayesian topics in a single, integrated resource.

A GUIded Toolkit using R

Introduction to Bayesian Econometrics

The Practice of Statistics for the AP® Course

Autres titres sur ce thème