Über uns

À propos de nous

Nachhaltigkeit

Durabilité

Open Access

Karriere

Carrière

Datenschutz

Protection des données

Impressum

Mentions légales

Zahlung und Lieferung

Paiement et livraison

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

Publikationen

Publications

Literatur

Onlinemodule LEXIA

Modules en ligne LEXIA

Bücher

Livres

Buchreihen

Collections

Datenbanken

recht.ch

Studienliteratur

Littérature pour les études

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

Autorenstimmen

Témoignages

Verlag

Éditions

Themen

Mehr wissen

Mitarbeitende

Collaborateurs

Geschichte

Histoire

Vision und Mission

Vision et mission

Unser Magazin

Notre magazine

Unternehmenskultur

Culture d'entreprise

Partner

Partenaires

Kontakt

Contact

Shop

Schweizer Recht

Ausländisches Recht

Über unseren Shop

Neuerscheinungen

Nouveautés

Sortiment und Funktionen

Assortiment et recherche

Vorankündigungen

À paraître

Nach Rechtsgebiet

Par domaine juridique

Kommentare

Commentaires

Müller J: Dynamics of Democratic
Elections

Dynamics of Democratic Elections

Data Science and Interactive Visualization Tools for the Analysis of Qualitative Evidence empowers qualitative and mixed methods researchers in the data science movement by offering no-code, cost-free software access so that they can apply cutting-edge and innovative methods to synthetize qualitative data. The book builds on the idea that qualitative and mixed methods researchers should not have to learn to code to benefit from rigorous open-source, cost-free software that uses artificial intelligence, machine learning, and data visualization tools-just as people do not need to know C++ or TypeScript to benefit from Microsoft Word. The real barrier is the hundreds of R code lines required to apply these concepts to their databases. By removing the coding proficiency hurdle, this book will empower their research endeavors and help them become active members of and contributors to the applied data science community. The book offers a comprehensive explanation of data science and machine learning methodologies, along with access to software application tools to implement these techniques without any coding proficiency. The book addresses the need for innovative tools that enable researchers to tap into the insights that come out of cutting-edge data science tools with absolutely no computer language literacy requirements.

Data Science and Interactive Visualization Tools for the Analysis of Qualitative Evidence

Chicago Price Theory, 2nd Edition

Probability theory is a discipline that studies the quantitative regularity of random phenomena. The fact that random phenomena arise, especially in the era of big data and artificial intelligence, determines the importance of this discipline. This volume introduces various concepts that quantitatively describe random phenomena, including probability, random variables, distribution functions, density functions, mathematical expectations, variances, moments, and characteristic functions. It finishes off by presenting probability limit theory, including various convergences.Throughout the volume, great importance is attached to the elaboration of probability thoughts. For this reason, some practical examples to illustrate the introduced concept are always used. In order to meet the needs of different levels of readers, there is a section on Supplements and Notes at the end of each chapter to enhance and expand the content in the body of the textbook. This volume contains a large number of problems of varying levels for the reader with the purpose to review, consolidate, deepen and expand their knowledge.As the only branch of mathematics that studies the quantitative regularity of random phenomena, probability theory has not only theoretical significance, but it is also a main theoretical basis of mathematical statistics. Therefore, it will be of interest to scholars from other disciplines related to random phenomena.

Probability Theory: An Elementary Course

Mathematics and Biomedical Engineering in Medicine, Physiology and Health Sciences is a visionary book, whose purpose is to demonstrate how fundamental mathematics and biomedical engineering can be incorporated into physiology, medicine, and health sciences teaching, research, and clinical practice to make these disciplines more quantitative and computational, and hence more explanatory and informative. The book also provides more quantitative formulation of medical procedures, towards supporting the growing field of precision medicine.

Mathematics and Biomedical Engineering in Medicine, Physiology and Health Sciences

This comprehensive compendium addresses a critical need in the AI and machine learning era by bridging foundational information theory (IT) concepts with practical applications in statistical learning. Unlike traditional IT textbooks, this volume emphasizes how IT principles, such as entropy and source coding, underpin modern machine learning techniques like cross-entropy, decision trees, and evidence-lower bounds.This unique book connects IT with probabilistic inference, illustrated through real-world applications, such as decoding LDPC codes for error correction. The inclusion of the Lea probabilistic programming package is particularly valuable for pedagogy, offering students a hands-on tool to solve numerical problems and reinforce theoretical concepts.The useful reference text benefits professionals, researchers, academics and students in the fields of calculus and probability, communications and information sciences.

Introduction To Information Theory And Probabilistic Inference, An

This book illustrates statistics with the R programming language, an emerging tool in data science. Filled with techniques for data science and analytics programming, the book is written for students and professionals. It is focused on learning outcomes and problem solving.

Demystifying Probability and Statistics for Data Scientists with R

Chapman & Hall/CRC Monographs on Statistics and Applied Probability

Significant new challenges to the use of likelihood-based methods for inference have helped to generate considerable interest in alternative inference methods that are not based on a full likelihood specification. This book provides a comprehensive survey of likelihood methods in statistics, with an emphasis on developments to inference functions for use in complex data. These inference functions are usually motivated by considerations related to likelihood-type arguments and have a variety of names, including composite likelihood, quasi-likelihood and pseudo-likelihood.

Likelihood and its Extensions

The Perils of Seeing Politics as a Sport

This Is No Game

While the field of Deep Learning has been advancing at frightening speeds during the last ten years, we do not have a plausible theoretical explanation of its success yet. One can safely say that Deep Learning works, but nobody really understands why. Nevertheless, starting around five years before now, a decent number of theoretical papers specifically concerning neural nets started to emerge at top machine learning venues. This makes us think that the core of Deep Learning theory has already started to crystallize.The goal of the present book is to present these core concepts of Deep Learning theory to readers so that they could directly dive into recent papers of this area. For this purpose, each chapter elaborates a simple model or a classical result in details first and then discusses possible generalizations and more recent developments of the same idea.We have to warn the reader that the present book is not a mathematical manuscript. Not all results here are stated as formal theorems and not all theorems are provided with complete and rigorous proofs. Many of the theorems of the book are proven up to some technical lemmas, while for some there is only a proof sketch. This conforms with the main idea of the book: present and illustrate concepts rather than reproduce all the results.The book, in its present form, covers the following topics: uniform generalization bounds, PAC-bayesian generalization bounds, double descent phenomena, infinitely-wide networks, implicit bias of gradient descent, loss landscape, gradient descent convergence guarantees, and initialization strategies.

Elements Of Deep Learning Theory

Voters need information about the qualities of candidates, the policies they propose and the partisan labels they wear. Does our Electoral College encourage the most qualified candidates to run? Does it force them to talk candidly with the American people? Will election results accurately reflect the needs of the population as a whole? Wayne's The Road to the White House 2024 answers these questions in a straightforward, jargon-free manner. Equipping you with a background on presidential elections and a guide to the 2024 election, it illustrates the strengths and weaknesses of our electoral democracy. It also offers insights on changes that have revolutionized contemporary electoral politics: the flood of money, the ideological polarization of American parties, the digital revolution, foreign interference, voter eligibility, the integrity of the voting process itself and much more.

Dynamics of Democratic Elections

Autres titres sur ce thème