Posts Tagged ‘ Bayesian ’

Video Introduction to Bayesian Data Analysis, Part 3: How to do Bayes?

May 8, 2017
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Video Introduction to Bayesian Data Analysis, Part 3: How to do Bayes?

This is the last video of a three part introduction to Bayesian data analysis aimed at you who isn’t necessarily that well-versed in probability theory but that do know a little bit of programming. If you haven’t watched the other parts yet, I re...

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Freudian hypothesis testing

March 23, 2017
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Freudian hypothesis testing

In his paper Mindless statistics, Gerd Gigerenzer uses a Freudian analogy to describe the mental conflict researchers experience over statistical hypothesis testing. He says that the “statistical ritual” of NHST (null hypothesis significance testing) “is a form of conflict resolution, like compulsive hand washing.” In Gigerenzer’s analogy, the id represents Bayesian analysis. Deep down, a […]

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Video Introduction to Bayesian Data Analysis, Part 2: Why use Bayes?

February 27, 2017
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Video Introduction to Bayesian Data Analysis, Part 2: Why use Bayes?

This is video two of a three part introduction to Bayesian data analysis aimed at you who isn’t necessarily that well-versed in probability theory but that do know a little bit of programming. If you haven’t watched part one yet, I really recomme...

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Video Introduction to Bayesian Data Analysis, Part 1: What is Bayes?

February 13, 2017
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Video Introduction to Bayesian Data Analysis, Part 1: What is Bayes?

This is video one of a three part introduction to Bayesian data analysis aimed at you who isn’t necessarily that well-versed in probability theory but that do know a little bit of programming. I gave a version of this tutorial at the UseR 2015 conf...

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Beginners Exercise: Bayesian Computation with Stan and Farmer Jöns

January 15, 2017
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Beginners Exercise: Bayesian Computation with Stan and Farmer Jöns

Over the last two years I’ve occasionally been giving a very basic tutorial to Bayesian statistics using R and Stan. At the end of the tutorial I hand out an exercise for those that want to flex their newly acquired skills. I call this exercise Bay...

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Subjectivity in statistics

December 15, 2016
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Andrew Gelman on subjectivity in statistics: Bayesian methods are often characterized as “subjective” because the user must choose a prior distribution, that is, a mathematical expression of prior information. The prior distribution requires information and user input, that’s for sure, but I don’t see this as being any more “subjective” than other aspects of a […]

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Cute Gibbs sampling for rounded observations

November 1, 2016
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Cute Gibbs sampling for rounded observations

I was attending a course of Bayesian Statistics where this problem showed up: There is a number of individuals, say 12, who take a pass/fail test 15 times. For each individual we have recorded the number of passes, which can go from 0 to 15. Because of confidentiality issues, we are presented with rounded-to-the-closest-multiple-of-3 data […]

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The Bayesian approach to ridge regression

October 30, 2016
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The Bayesian approach to ridge regression

In a previous post, we demonstrated that ridge regression (a form of regularized linear regression that attempts to shrink the beta coefficients toward zero) can be super-effective at combating overfitting and lead to a greatly more generalizable model. This approach… Continue reading →

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Notes from 4th Bayesian Mixer Meetup

October 4, 2016
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Notes from 4th Bayesian Mixer Meetup

Last Tuesday we got together for the 4th Bayesian Mixer Meetup. Product Madness kindly hosted us at their offices in Euston Square. About 50 Bayesians came along; the biggest turn up thus far, including developers of PyMC3 (Peadar Coyle) and Stan (Mich...

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Fitting a distribution in Stan from scratch

September 27, 2016
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Fitting a distribution in Stan from scratch

Last week the French National Institute of Health and Medical Research (Inserm) organised with the Stan Group a training programme on Bayesian Inference with Stan for Pharmacometrics in Paris. Daniel Lee and Michael Betancourt, who run the course over ...

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