# Posts Tagged ‘ Bayesian ’

## More data, less accuracy

January 27, 2015
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Statistical methods should do better with more data. That’s essentially what the technical term “consistency” means. But with improper numerical techniques, the the numerical error can increase with more data, overshadowing the decreasing statistical error. There are three ways Bayesian posterior probability calculations can degrade with more data: Polynomial approximation Missing the spike Underflow Elementary numerical integration algorithms, […]

## Extended Kalman filter example in R

January 13, 2015
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Last week's post about the Kalman filter focused on the derivation of the algorithm. Today I will continue with the extended Kalman filter (EKF) that can deal also with nonlinearities. According to Wikipedia the EKF has been considered the de facto sta...

## Probable Points and Credible Intervals, Part 2: Decision Theory

January 7, 2015
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“Behind every great point estimate stands a minimized loss function.” – Me, just now This is a continuation of Probable Points and Credible Intervals, a series of posts on Bayesian point and interval estimates. In Part 1 we looked at these...

## Kalman filter example visualised with R

January 6, 2015
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At the last Cologne R user meeting Holger Zien gave a great introduction to dynamic linear models (dlm). One special case of a dlm is the Kalman filter, which I will discuss in this post in more detail. I kind of used it earlier when I measured the tem...

## R Code for Election Posterior Distribution From a Random Sample

January 5, 2015
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I wrote a summary article a couple of years ago discussing some probability aspects of the 2012 Presidential general election with a particular focus on exit polling. I’ve had a few people email me asking for the code I used in some if the examples. I have used this code since before the 2008 elections so […]

## One-way ANOVA with fixed and random effects from a Bayesian perspective

December 22, 2014
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This blog post is derived from a computer practical session that I ran as part of my new course on Statistics for Big Data, previously discussed. This course covered a lot of material very quickly. In particular, I deferred introducing notions of hierarchical modelling until the Bayesian part of the course, where I feel it […]

## Notes from the Kölner R meeting, 12 December 2014

December 16, 2014
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Last week's Cologne R user group meeting was the best attended so far, and it was a remarkable event - I believe not a single line of R code was shown. Still, it was an R user group meeting with two excellent talks, and you will understand shortly why ...

## Measuring temperature with my Arduino

December 2, 2014
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It is really getting colder in London - it is now about 5°C outside. The heating is on and I have got better at measuring the temperature at home as well. Or, so I believe.Last week's approach of me guessing/feeling the temperature combined with an...

## 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 the...