Posts Tagged ‘ Bayesian ’

Confidence vs. Credibility Intervals

November 26, 2014
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Tomorrow, for the final lecture of the Mathematical Statistics course, I will try to illustrate - using Monte Carlo simulations - the difference between classical statistics, and the Bayesien approach. The (simple) way I see it is the following, for frequentists, a probability is a measure of the the frequency of repeated events, so the interpretation is that parameters are fixed (but unknown), and data are random for Bayesians, a probability…

How cold is it? A Bayesian attempt to measure temperature

November 25, 2014
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It is getting colder in London, yet it is still quite mild considering that it is late November. Well, indoors it still feels like 20°C (68°F) to me, but I have been told last week that I should switch on the heating. Luckily I found an old thermometer to check. The thermometer showed 18°C. Is it really below 20°C? The thermometer is quite old and I'm not sure that is…

How to Summarize a 2D Posterior Using a Highest Density Ellipse

November 13, 2014
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Making a slight digression from last month’s Probable Points and Credible Intervals here is how to summarize a 2D posterior density using a highest density ellipse. This is a straight forward extension of the highest density interval to the situati...

Probable Points and Credible Intervals, Part 1

October 26, 2014
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After having broken the Bayesian eggs and prepared your model in your statistical kitchen the main dish is the posterior. The posterior is the posterior is the posterior, given the model and the data it contains all the information you need and anyth...

Tiny Data, Approximate Bayesian Computation and the Socks of Karl Broman

October 20, 2014
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Big data is all the rage, but sometimes you don’t have big data. Sometimes you don’t even have average size data. Sometimes you only have eleven unique socks: Karl Broman is here putting forward a very interesting problem. Interesting, not onl...

Bayesian First Aid: Poisson Test

September 5, 2014
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As the normal distribution is sort of the default choice when modeling continuous data (but not necessarily the best choice), the Poisson distribution is the default when modeling counts of events. Indeed, when all you know is the number of events du...

Recent Articles

August 20, 2014
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I have uploaded a few papers I have written and presented at some national conferences over the past several years.  Currently, all the articles relate to election research.

Hit and run. Think Bayes!

July 29, 2014
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At the R in Insurance conference Arthur Charpentier gave a great keynote talk on Bayesian modelling in R. Bayes' theorem on conditional probabilities is strikingly simple, yet incredibly thought provoking. Here is an example from Daniel Kahneman to tes...

Bayesian Wizardry for Muggles

July 11, 2014
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Monday, I will be giving the closing talk of the R in Insurance Conference, in London, on Bayesian Computations for Actuaries, as to be more specific, Getting into Bayesian Wizardry... (with the eyes of a muggle actuary). The animated version of the slides (since we will spend some time on MCMC algorithm, I thought that animated graphs could be more informative) can be downloaded from here. Those slides are based…

Automatic bias correction doesn’t fix omitted variable bias

July 8, 2014
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Page 94 of Gelman, Carlin, Stern, Dunson, Vehtari, Rubin “Bayesian Data Analysis” 3rd Edition (which we will call BDA3) provides a great example of what happens when common broad frequentist bias criticisms are over-applied to predictions from ordinary linear regression: the predictions appear to fall apart. BDA3 goes on to exhibit what might be considered […] Related posts: Frequentist inference only seems easy Six Fundamental Methods to Generate a Random…