1132 search results for "parallel"

A brief introduction to higher order functions in R

March 14, 2014
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In R, function may not be as special as it is in other programming languages; it is regarded as one of the many types and can be passed as an argument to some other function. The way we deal with other objects such list and data.frame definitely applies to function. Here is a simple example in which we define...

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A brief introduction to higher order functions in R

March 14, 2014
By

In R, function may not be as special as it is in other programming languages; it is regarded as one of the many types and can be passed as an argument to some other function. The way we deal with other objects such list and data.frame definitely applies to function. Here is a simple example in which we define...

Read more »

How to use Bioconductor to find empirical evidence in support of π being a normal number

March 14, 2014
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How to use Bioconductor to find empirical evidence in support of π being a normal number

Happy π day everybody! I wanted to write some simple code (included below) to the test parallelization capabilities of my  new cluster. So, in honor of  π day, I decided to check for evidence that π is a normal number. A … Continue reading →

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where did the normalising constants go?! [part 2]

March 11, 2014
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where did the normalising constants go?! [part 2]

Coming (swiftly and smoothly) back home after this wonderful and intense week in Banff, I hugged my loved ones,  quickly unpacked, ran a washing machine, and  then sat down to check where and how my reasoning was wrong. To start with, I experimented with a toy example in R: and (of course!) it produced the

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where did the normalising constants go?! [part 1]

March 10, 2014
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where did the normalising constants go?! [part 1]

When listening this week to several talks in Banff handling large datasets or complex likelihoods by parallelisation, splitting the posterior as and handling each term of this product on a separate processor or thread as proportional to a probability density, then producing simulations from the mi‘s and attempting at deriving simulations from the original product,

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R 3.0.3 is released

March 10, 2014
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R 3.0.3 is released

R 3.0.3 (codename “Warm Puppy) was released several days ago. The full list of new features and bug fixes is provided below. Upgrading to R 3.0.3 You can download the latest version from here. Or, if you are using Windows, you can upgrade to the latest version using the installr package. Simply run the following

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Advances in scalable Bayesian computation [day #4]

March 7, 2014
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Advances in scalable Bayesian computation [day #4]

Final day of our workshop Advances in Scalable Bayesian Computation already, since tomorrow morning is an open research time ½ day! Another “perfect day in paradise”, with the Banff Centre campus covered by a fine snow blanket, still falling…, and making work in an office of BIRS a dream-like moment. Still looking for a daily theme,

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Advances in scalable Bayesian computation [day #3]

March 6, 2014
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Advances in scalable Bayesian computation [day #3]

We have now gone over the midpoint of our workshop Advances in Scalable Bayesian Computation with three talks in the morning and an open research or open air afternoon. (Maybe surprisingly I chose to stay indoors and work on a new research topic rather than trying cross-country skiing!) If I must give a theme for

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Foundations of Statistical Algorithms [book review]

February 27, 2014
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Foundations of Statistical Algorithms [book review]

There is computational statistics and there is statistical computing. And then there is statistical algorithmic. Not the same thing, by far. This 2014 book by Weihs, Mersman and Ligges, from TU Dortmund, the later being also a member of the R Core team, stands at one end of this wide spectrum of techniques required by

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evaluating stochastic algorithms

February 19, 2014
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evaluating stochastic algorithms

Reinaldo sent me this email a long while ago Could you recommend me a nice reference about measures to evaluate stochastic algorithms (in particular focus in approximating posterior distributions). and I hope he is still reading the ‘Og, despite my lack of prompt reply! I procrastinated and procrastinated in answering this question as I did not

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