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Prell M: The DnA of Statistics

The DnA of Statistics

Introduces Aristotelian realist philosophy of mathematics through the work of leading Australian mathematician and historian of ideas, James Franklin.

Connecting Philosophy of Mathematics, Ethics and Probability

The Necessities Underlying Reality

Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. The text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data.

Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. The text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data.

What If

Causal Inference

How We Gave Computers the Power to Choose for Us

The Irrational Decision

Navigating Across Mathematical Cultures And Times: Exploring The Diversity Of Discoveries And Proofs

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

Advances in Reliability Science

Stochastic Modeling and Statistical Methods: Advances and Applications is the practical guide to the latest developments in data analysis and research methods. The book explores the significant research progress that has been seen in recent decades, offering vital tools for analyzing modern applications and real data. Topics covered include Dynamic Reliability, Stochastic Modeling, System Maintainability, and Parametric, Semi-Parametric, and Nonparametric Statistical Inference. Readers will find the latest advancements in these areas, making it an essential resource for researchers and practitioners who want to explore these evolving fields and stay updated on cutting-edge research.

Advances and Applications

Stochastic Modeling and Statistical Methods

Indo-Arabic Numerals and the Making of Practical Knowledge in Early Modern Europe

Working Numbers

The specialized field of scan statistics, fathered by Joseph Naus around 1999, burgeoned rapidly to prominence in the field of applied probability and statistics. In additional to challenging theoretical probelms, scan statistics has exciting applications in many areas of science and technologyu including archaelogy, astronomy, physics, bioinformatics, and food sciences, just to name a few.In many fields, decision makers give a great deal of weight to clusters of events. Public Health investigators look for common cause factors to explain clusters of, for example, cancer. Molecular biologists look for palindrome clusters in DNA for clues as to the origin of replication viruses. Telecommunication engineers design capacity to accommodate clusters of calls being dialed simultaneously to a switchboard. Quality control experts investigate clusters of defects. The probabilities of different types of clusters&nbsp; under various conditions are tools of the physical, natural, and social sciences. Scan statistics arise naturally in the scanning of time and space, seeking clusters of events. It is therefore no surprise that scan statistics is a major area of research in probability and statistics in the 21st century. In the last 5 years about 1600 hits appear on Google Scholar referencing the extensive activity in scan statistics and the breadth of the application. (Since 2010, about 482 hits are recorded in Google scholar.)The Handbook of Scan Statistics in two volumes is intended for researchers in probability and statistics and scientists in several areas including biology, engineering, health, medical, and social sciences. It will be of great value to graduate students in statistics and in all areas where scan statistics are used.

The “Handbook of Scan Statistics” in two volumes is intended for researchers in probability and statistics and scientists in several areas including biology, engineering, health, medical, and social sciences. It will be of great value to graduate students in statistics and in all areas where scan statistics are used.The specialized field&nbsp;called&nbsp;'scan statistics', fathered by Joseph Naus around 1999, burgeoned rapidly to prominence in the broader fields of applied probability and statistics. In additional to challenging theoretical problems, scan statistics has exciting applications in many areas of science and technology including archaeology, astronomy, physics, bioinformatics, and food sciences, just to name a few.In many fields, decision makers give a great deal of weight to clusters of events. Public Health investigators look for common cause factors to explain clusters of, for example, cancer. Molecular biologists look for palindrome clusters in DNA for clues as to the origin of replication viruses. Telecommunication engineers design capacity to accommodate clusters of calls being dialed simultaneously to a switchboard. Quality control experts investigate clusters of defects. The probabilities of different types of clusters&nbsp; under various conditions are tools of the physical, natural, and social sciences. Scan statistics arise naturally in the scanning of time and space, seeking clusters of events. It is therefore no surprise that scan statistics is a major area of research in probability and statistics in the 21st century.

Handbook of Scan Statistics

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.

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.

Social Media Observatory

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.

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.

The DnA of Statistics

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