Join us at Bayes Business School (formerly Cass) for the book launch of "Everything Is Predictable: How Bayes' Remarkable Theorem Explains the World" by Tom Chivers.
Tom will discuss the book with Sir David Spiegelhalter FRS OBE, Emeritus Professor of Statistics, Centre for Mathematical Science, University of Cambridge and Mel Zhang, Head of Algorithmic Pricing, Ki.
The event will be opened by Professor Andre Spicer, Dean of Bayes Business School, with the discussion chaired by Markus Gesmann, co-founder, Insurance Capital Market Research and Honorary Visiting Fellow at Bayes Business School.
About the book
Thomas Bayes was an eighteenth-century Presbyterian minister and amateur mathematician whose obscure life belied the profound impact of his work. Like most research into probability at the time, his theorem was mainly seen as relevant to games of chance, like dice and cards. But its implications soon became clear.
Bayes' theorem helps explain why highly accurate screening tests can lead to false positives, causing unnecessary anxiety for patients. A failure to account for it in court has put innocent people in jail. But its influence goes far beyond practical applications. A cornerstone of rational thought, Bayesian principles are used in modelling and forecasting. 'Superforecasters', a group of expert predictors who outperform CIA analysts, use a Bayesian approach. And many argue that Bayes' theorem is not just a useful tool, but a description of almost everything - that it is the underlying architecture of rationality, and of the human brain.
Fusing biography, razor-sharp science communication and intellectual history, Everything Is Predictable is a captivating tour of Bayes' theorem and its impact on modern life. From medical testing to artificial intelligence, Tom Chivers shows how a single compelling idea can have far-reaching consequences.
Panellists
Tom Chivers, Science Writer and Author, Semafor
X: @TomChivers
Tom Chivers is a science writer and author with Semafor. He was named the Association of British Science Writers' science writer of the year in 2020 and has won two "statistical excellence in journalism" awards from the Royal Statistical Society. Everything is Predictable is his third book; his first, The Rationalist's Guide to the Galaxy, was named a Times book of the year in 2019.
Sir David Spiegelhalter FRS OBE, Emeritus Professor of Statistics, Centre for Mathematical Science, University of Cambridge
X: @d_spiegel
Photo Credit: Amanda Benson
Professor Sir David Spiegelhalter FRS OBE is Emeritus Professor of Statistics in the Centre for Mathematical Sciences at the University of Cambridge, and a Non-Executive Director of the UK Statistics Authority. His bestselling book, The Art of Statistics, was published in March 2019. He has 50 years experience of researching and promoting Bayesian ideas.
Mel Zhang, Head of Algorithmic Pricing, Ki
Melanie is the Head of Algorithmic Pricing at Ki, the first fully digital and algorithmically-driven Lloyd’s of London syndicate. Prior to Ki, she has worked in various actuarial and innovation roles within the Lloyd’s insurance market over the last 12 years, including at Brit Insurance and AXIS Capital.
Melanie is a Cambridge and UCL alumni and a Fellow of the Institute and Faculty of Actuaries.
Chair
Markus Gesmann, co-founder, Insurance Capital Market Research and Honorary Visiting Fellow at Bayes Business School
Markus Gesmann is the co-founder of Insurance Capital Market Research, a quantitative research house based in London. He has spent over 20 years in both insurance and capital markets. Markus is the former head of analysis at Lloyd’s of London, where he set up a market wide analytical performance and price monitoring framework. While at Lloyd’s he developed tools to assess the credibility of business plans, using Bayesian models.
Markus is an expert in modelling non-life insurance portfolios and probabilistic programming, and an Honorary Visiting Fellow at Bayes Business School, City, University of London. He is also the co-founder of the Insurance Data Science conference series and the Bayesian Mixer meetups.
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