The Equation of Knowledge

From Bayes' Rule to a Unified Philosophy of Science
 
 
Chapman & Hall/CRC (Verlag)
  • 1. Auflage
  • |
  • erschienen am 18. Juni 2020
  • |
  • 460 Seiten
 
E-Book | ePUB ohne DRM | Systemvoraussetzungen
978-1-000-06327-1 (ISBN)
 

The Equation of Knowledge: From Bayes' Rule to a Unified Philosophy of Science introduces readers to the Bayesian approach to science: teasing out the link between probability and knowledge.

The author strives to make this book accessible to a very broad audience, suitable for professionals, students, and academics, as well as the enthusiastic amateur scientist/mathematician.

This book also shows how Bayesianism sheds new light on nearly all areas of knowledge, from philosophy to mathematics, science and engineering, but also law, politics and everyday decision-making.

Bayesian thinking is an important topic for research, which has seen dramatic progress in the recent years, and has a significant role to play in the understanding and development of AI and Machine Learning, among many other things. This book seeks to act as a tool for proselytising the benefits and limits of Bayesianism to a wider public.

Features

    • Presents the Bayesian approach as a unifying scientific method for a wide range of topics

    • Suitable for a broad audience, including professionals, students, and academics

    • Provides a more accessible, philosophical introduction to the subject that is offered elsewhere

    1. Auflage
    • Englisch
    • Milton
    • |
    • Großbritannien
    Taylor & Francis Ltd
    • Für höhere Schule und Studium
    12 schwarz-weiße Abbildungen, 2 schwarz-weiße Tabellen
    • 4,77 MB
    978-1-000-06327-1 (9781000063271)
    weitere Ausgaben werden ermittelt

    Lê Nguyên graduated from the École Polytechnique de Montréal with a PhD in applied mathematics, before working as a post-doctoral researcher at MIT. Since 2016, he has been working as a science communicator at EPFL. He also has his own YouTube channel Science4All (in French), with over 170k subscribers.

    Sction I. Pure Bayesianism. 1. On A Transformative Journey. 2. Bayes Theorem. 3. Logically Speaking... 4. Let's Generalize! 5. All Hail Prejudices. 6. The Bayesian Prophets. 7. Solomonoff's Demon. Section II. Applied Bayesianism. 8. Can You Keep A Secret? 9. Game, Set and Math. 10. Will Darwin Select Bayes? 11. Exponentially Counter-Intuitive. 12. Ockham Cuts to the Chase. 13. Facts Are Misleading. Section III. Pragmatic Bayesianism. 14. Quick And Not Too Dirty. 15. Wish Me Luck. 16. Down Memory Lane. 17. Let's Sleep On It. 18. The Unreasonable Effectiveness of Abstraction. 19. The Bayesian Brain. Section IV. Beyond Bayesianism. 20. It's All Fiction. 21. Exploring The Origins Of Beliefs. 22. Beyond Bayesianism.
    "[. . . ] Trained in the hard school of online videos, Le Nguyen Hoang has found a new tone to talk about science, a tone that is both rigorous and narrative, where examples illuminate the most abstract questions."


    - From the Foreword by Gilles Dowek, Professor at Ecole Polytechnique and researcher at the Laboratoire d'Informatique de l'Ecole Polytechnique and the Institut National de Recherche en Informatique et en Automatique (INRIA).





    Le Nguyen Hoang takes us on a fascinating intellectual journey into Bayesianism, cutting across many social and natural sciences. The Equation of Knowledge: From Bayes' Rule to a Unified Philosophy of Science is a real page turner.


    -George Zaccour, HEC Montreal and co-author of Handbook of Dynamic Game Theory


    "Making math accessible to everyone, showing its connections with dozens of different domains, narrating scientific discovery as a personal human adventure, and sharing impressive enthusiasm: there is definitely something of Greg Chaitin's Meta Math! in Le Nguyen Hoang's book!"


    - Remi Peyre, Ecole des Mines de Nancy








    "A remarkable piece of work, broad and insightful at the same time. This book is unique in that it gives an accessible journey from subtle probabilistic puzzles to the most advanced concepts at the heart of the machine learning revolution; with unrivalled clarity, it exposes deep ideas that have remained very confidential outside of specialized circles, and that yet are becoming fundamental in the way we understand our world."


    -Clement Hongler, Associate Professor and Chair of Statistical Field Theory, EPFL


    "As someone who practices research and publishes academic papers, it is frustrating to note how little we, scientists, are trained in epistemology. 'How do we know that we know?' This question is often neglected or taken for granted. The recent controversies about reproducibility of scientific publishing might be one of the tips of a larger iceberg. This book will, in my opinion, be remembered as one of those that helped melt the iceberg."


    -El Mahdi El Mhamdi, Ecole Polytechnique Federale de Lausanne.

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