Search results for: “bayes”
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Continuing my recent, seemingly interminable, series of too-technical posts on probability theory… To understand this one you’ll need to remember Bayes’ Theorem, and the resulting need for a Bayesian statistician to come up with an appropriate prior distribution to describe her state of knowledge in the absence of the experimental data she is considering, updated
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[Apologies — this is long, technical, and there are too few examples. I am putting it out for commentary more than anything else…] In some recent articles and blog posts (including one in response to astronomer David Hogg), Columbia University statistician Andrew Gelman has outlined the philosophical position that he and some of his colleagues
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Embarrassing update: as pointed out by Vladimir Nesov in the comments, all of my quantitative points below are incorrect. To maximize expected winnings, you should bet on whichever alternative you judge to be most likely. If you have a so-called logarithmic utility function — which already has the property of growing faster for small amounts
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I’ve come across a couple bits of popular/political culture that give me the opportunity to discuss one of my favorite topics: the uses and abuses of probability theory. The first is piece by Nate Silver of the New York Times’ FiveThirtyEight blog, dedicated to trying to crunch the political numbers of polls and other data
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One of my holiday treks this year was across town to visit Bunhill Fields, final resting place of two of my favorite Londoners: William Blake and Thomas Bayes. Blake is of course one of the most famous poets in the English language, but most people know him only from short poems like The Tiger [sic]
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The perfect stocking-stuffer for that would-be Bayesian cosmologist you’ve been shopping for: As readers here will know, the Bayesian view of probability is just that probabilities are statements about our knowledge of the world, and thus eminently suited to use in scientific inquiry (indeed, this is really the only consistent way to make probabilistic statements
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In today’s Sunday NY Times Magazine, there’s a long article by psychologist Steven Pinker, on “Personal Genomics”, the growing ability for individuals to get information about their genetic inheritance. He discusses the evolution of psychological traits versus intelligence, and highlights the complicated interaction amongst genes, and between genes and society. But what caught my eye
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The Random Universe: How Models and Probability Help Us Make Sense of the Cosmos is available now! The Random Universe tracks the story of how scientists use data to interpret and model the universe. It fuses philosophy and the history of science, beginning with David Hume’s 17th century question “can we know anything more than
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I am a cosmologist and astrophysicist, in the Department of Physics at Imperial College London, where I am also the Director of the Imperial Centre for Inference and Cosmology. I am especially interested in the interpretation and analysis of cosmological data using principled Bayesian statistical techniques. I have written a book, The Random Universe: How
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This week, we released (most of) the final set of papers from the Planck collaboration — the long-awaited Planck 2018 results (which were originally meant to be the “Planck 2016 results”, but everything takes longer than you hope…), available on the ESA website as well as the arXiv. More importantly for many astrophysicists and cosmologists,