Posts Tagged ‘ statistics ’

weakly informative reparameterisations for location-scale mixtures

January 18, 2017
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weakly informative reparameterisations for location-scale mixtures

We have been working towards a revision of our reparameterisation paper for quite a while now and too advantage of Kate Lee visiting Paris this fortnight to make a final round: we have now arXived (and submitted) the new version. The major change against the earlier version is the extension of the approach to a […]

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Good models + Bad data = Bad analysis

January 18, 2017
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Good models + Bad data = Bad analysis

Example showing how to diagnose bad data in data science models

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The Class-size debate – it matters to teachers

January 16, 2017
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The Class-size debate – it matters to teachers

Class size matters to teachers Class size is a perennial question in education. What is the ideal size for a school class? Teachers would like smaller classes, to improve learning. There is evidence of a small positive effect size due … Continue reading →

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Beginners Exercise: Bayesian Computation with Stan and Farmer Jöns

January 14, 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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Why people hate statistics

January 10, 2017
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Why people hate statistics

This summer/Christmas break it has been my pleasure to help a young woman who is struggling with statistics, and it has prompted me to ask people who teach postgraduate statistical methods – WTF are you doing? Louise (name changed) is a … Continue reading →

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Why do Decision Trees Work?

January 6, 2017
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Why do Decision Trees Work?

In this article we will discuss the machine learning method called “decision trees”, moving quickly over the usual “how decision trees work” and spending time on “why decision trees work.” We will write from a computational learning theory perspective, and hope this helps make both decision trees and computational learning theory more comprehensible. The goal … Continue reading Why do Decision Trees Work?

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truncated normal algorithms

January 3, 2017
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truncated normal algorithms

Nicolas Chopin (CREST) just posted an entry on Statisfaction about the comparison of truncated Normal algorithms run by Alan Rogers, from the University of Utah. Nicolas wrote a paper in Statistics and Computing about a simulation method, which proposes a Ziggurat type of algorithm for this purpose, and which I do not remember reading, thanks […]

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Forecasting Natural Catastrophes (is rather difficult)

January 2, 2017
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Forecasting Natural Catastrophes (is rather difficult)

Following my previous post, I wanted to spend more time, on the time series with “global weather-related disaster losses as a proportion of global GDP” over the time period 1990-2016 that Roger Pilke sent me last night. db=data.frame(year=1990:2016, ratio=c(.23,.27,.32,.37,.22,.26,.29,.15,.40,.28,.14,.09,.24,.18,.29,.51,.13,.17,.25,.13,.21,.29,.25,.2,.15,.12,.12)) In my previous post, I spend some time explaining that we should provide some sort of ‘confidence interval’ when we try to predict a pattern. That was what we call ‘model…

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A Theory of Nested Cross Simulation

January 2, 2017
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A Theory of Nested Cross Simulation

[Reader’s Note. Some of our articles are applied and some of our articles are more theoretical. The following article is more theoretical, and requires fairly formal notation to even work through. However, it should be of interest as it touches on some of the fine points of cross-validation that are quite hard to perceive or … Continue reading A Theory of Nested Cross Simulation

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What is a Linear Trend, by the way?

January 2, 2017
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What is a Linear Trend, by the way?

I had a very strange discussion on twitter (yes, another one), about regression curves. I think it started with a tweet based on some xkcd picture (just for fun, because it was New Year’s Day) “don’t trust linear regressions” https://t.co/exUCvyRd1G pic.twitter.com/O6rBJfkULa — Arthur Charpentier (@freakonometrics) 1 janvier 2017 There were comments on that picture, by econometricians, mainly about ‘significant’ trends when datasets are very noisy. And I mentioned a graph…

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