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Prell M: What Are the Odds?

What Are the Odds?

A Statistical Guide to Certainty in an Uncertain World

Plato, Whitehead, Aristotle

On Mathematics, Unity, and the Good

Application of Python in Healthcare Technology: Medical Data Analysis and Developing ML/AI Models provides comprehensive coverage of the Python programming language from a medical-based perspective. With a strong focus on utility-based learning rather than technical-based learning of Python, this title clarifies how the application of Python in the healthcare technology industry has a meaningful impact on patient care through the development of deep learning models for early prediction of diseases and the development of Clinical Decision Support System (CDS). In 13 chapters, an extensive range of topics pertaining to the intersection of Python and medical sciences is covered. Coverage defines how to set up an environment to work with python, describes Python class, methods, and functions, explains both basic and advanced statistical analysis in Python, and provides a tool to assist in the development of deep learning models such as API, Git, Github, NumPy, Pandas, and SciPy.

Medical Data Analysis and Developing ML/AI Models

Application of Python in the Healthcare Technology

There is an increasing prevalence of large and complex datasets within the social sciences, and notably criminology in recent years. This data explosion has led to the development of specialized techniques for extracting information from such data. This book presents an introduction to "Big Data Science" for criminology and the social sciences, taking a case study-based approach to explaining the concepts. The theory is introduced as needed to answer scientific questions based on real data problems in the application areas. Some R and Python code is included to give support with implementation of the methods.

From Case Studies to Theory

Big Data Science for Criminology and the Social Sciences

Qualitative Sozialforschung

Gemeinsam zu interpretieren gehört in der deutschen qualitativen Sozialforschung seit Jahrzehnten zum Alltag der Forschung – ohne dass allerdings etwas Wesentliches über diesen Alltag der Gruppeninterpretationen bekannt ist. Das vorliegende Buch möchte deshalb die alltägliche Praxis der Gruppeninterpretation&nbsp;erstmalig sichtbar machen und reflektieren. Zu diesem Zweck wird&nbsp;ein systematischer Überblick über die Theorien und Praxen der Gruppeninterpretation und deren Spielarten&nbsp;gegeben. Dann wird ein Fall einer Gruppeninterpretation exemplarisch dokumentiert und reflektiert. Dabei werden vor allem die kommunikativen und sozialen Prozesse bei der Produktion und Konstruktion sozialwissenschaftlichen Wissens sichtbar(er) gemacht. So wird nicht nur das Reflexionswissen über dieses Verfahren gemehrt, sondern dieses Wissen ist hilfreich, wenn man selbst Gruppeninterpretationen&nbsp;durchführen oder daran teilnehmen möchte. Der Inhalt Gemeinsam Interpretieren als Kommunikationsprozess • Über den Alltag der gemeinsamen Auslegung • Wissenssoziologische Bildanalyse • Transkription einer Interpretationssitzung • Diese Familie ist Amerika! Eine hermeneutisch-wissenssoziologische Interpretation&nbsp;Die Zielgruppe Studierende und Dozierende aller Sozialwissenschaften, die sich mit Methoden der qualitativen Sozialforschung beschäftigen. Der Autor Dr. Jo Reichertz ist Professor für Kommunikationswissenschaft an der Universität Duisburg-Essen.&nbsp;

Die Gruppeninterpretation als kommunikativer Prozess

Gemeinsam interpretieren

Learn to build a web app that can monitor millions of posts across all social media platforms to chart public opinion in real time. Social media has become our modern-day agora, the place where we get our news and engage in political discourse. With disinformation, hate speech, foreign interference, authoritarianism, market monopolization, and political polarization all on the rise, monitoring social media in the public interest has never been so difficult nor so urgent. Social Media Observatory is an in-depth beginner’s guide to coding full-stack web applications in Python to track and analyze social media activity at scale, giving you unparalleled insights into the cultural and political conversations of our time, then empowering you to reach your own conclusions. You’ll start by coding a web app to monitor Russian disinformation channels discussing Ukraine on Telegram, then learn more adversarial methods to scrape the Wayback Machine, Instagram, and TikTok. You’ll detect Twitter bots following Saudi influencers, extract text-embedded URLs to reveal a white supremacist blogosphere, parse archived tweets to reveal the discourse leading up to the January 6 insurrection, and learn to circumvent Meta's and ByteDance's defenses to scrape Instagram and TikTok, collecting evidence of war crimes in the Middle East. Finally, you’ll write an API and deploy your observatory to a cloud server to collaborate with other investigators. By the end of the book, you’ll have a working website that allows collaborators to log in, request data collection, and perform analysis of collected data, via a point-and-click interface.&nbsp; Along the way, you’ll learn to: <ul><li>Use Python and RabbitMQ to retrieve Telegram data from a remote API</li><li>Store data with PostgreSQL and query it with SQLAlchemy</li><li>Analyze the data you’ve retrieved with Pandas, NetworkX, Gephi, and other tools</li><li>Use state-of-the-art AI tools to transcribe audio and classify text</li><li>Deploy a website with an interactive dashboard using FastAPI, Dash, Cytoscape, Uvicorn, Gunicorn, Nginx, AWS, and Let’s Encrypt</li><li>Containerize your apps with Docker and provision infrastructure with Pulumi</li></ul> Whether you’re a citizen scientist, a security researcher, a journalist, an activist, or a scholar, Social Media Observatory is an essential blueprint for building your window into online worlds.

Track Disinformation, Spot Bots, and Find Out What's Really Happening

Social Media Exposed

The Laws of Thought

The book is devoted to contemporary variance reduction methods for Monte Carlo and Markov Chain (MCMC) with Machine Learning and Finance applications. We present new approaches based on martingale representations, and Stein control variates besides the standard techniques. In addition, the book focuses on developing new variance reduction algorithms in MCMC and their theoretical analysis. Readers will find numerical examples that illustrate all proposed algorithms.

Advanced Variance Reduction Techniques For Mcmc

This book presents a comprehensive treatment of pricing and hedging soccer betting contracts using the modern techniques of probability, statistics, finance, and portfolio management. It introduces betting in general and then addresses the probabilistic intuition needed in the more complex world of soccer betting. The author presents an innovative perspective by thinking about betting odds in terms of the expected number of goals of both teams. In addition, when the betting market is in play, the book treats betting before the game and during the game separately.

Science of Soccer Statistics

Soccermetrics

Gibbs Measures In Machine Learning

Scaling (zooming in or out) is taking a close or distant view of an object. Infinite scaling (the scaling parameter tends to zero or infinity) may lead to a limit which is self-similar and much simpler than the original object. For instance, the classical Donsker's theorem says that all weakly dependent stationary processes with finite variance scale to Brownian motion at large scales. In the case of a random field indexed by two-dimensional parameter, both types of scaling can be anisotropic, meaning that the horizontal and vertical axes are scaled at different rate determined by the ratio γ&gt;0 of the scaling exponents along the axes. The natural questions under such scaling are whether the scaling limits exist for any γ&gt;0, and what are these limits are. The book tries to answer these questions. It introduces the concept of scaling transition and discusses its existence for a natural class of planar random fields including Gaussian, linear and some nonlinear ones. The scaling limits are identified and exhibit a surprising trichitomy at a critical point γ0, with 'unbalanced' limits having unusual path properties and dependence structure along one of the coordinate axes. Scaling transition occurs in applied sciences (telecommunications and econometrics) when aggregating independent processes with long-range dependence in which case 'unbalanced' limits are classical Gaussian or stable random fields.

Anisotropic Scaling Of Random Fields With Long-range Dependence: Scaling Limits Of Random Fields With Applications

This book introduces factor analysis methods for the study of multivariate stochastic processes whose structure evolves over time. It introduces the theory of factor analysis and evolutionary stochastic processes and combines that to study evolutionary factor models. The book includes many real examples to illustrate applications from such fields as macroeconomics, finance, neuroscience, communication, and psychology. MATLAB® and R code is included throughout the book for implementation of the methods.

Evolutionary Factor Analysis

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

What Are the Odds?

Informations bibliographiques

Mots-clés

Informations bibliographiques

Mots-clés

Autres titres sur ce thème