Questions tagged [computing]

For on-topic questions involving statistical computing. Please include also some statistical methods tag.

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162
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21answers
67k views

Does Julia have any hope of sticking in the statistical community?

I recently read a post from R-Bloggers, that linked to this blog post from John Myles White about a new language called Julia. Julia takes advantage of a just-in-time compiler that gives it wicked ...
36
votes
4answers
59k views

How to sample from a normal distribution with known mean and variance using a conventional programming language?

I've never had a course in statistics, so I hope I'm asking in the right place here. Suppose I have only two data describing a normal distribution: the mean $\mu$ and variance $\sigma^2$. I want to ...
1
vote
1answer
122 views

How to derive this version of variance formula

A probably very easy computational question, but I don't really understand how it's done: I try to compute the point estimates of a normal distribution as $N \sim (\beta, \sigma)$. Using the method ...
15
votes
9answers
5k views

What books provide an overview of computational statistics as it applies to computer science?

As a software engineer, I'm interested in topics such as statistical algorithms, data mining, machine learning, Bayesian networks, classification algorithms, neural networks, Markov chains, Monte ...
6
votes
1answer
3k views

What is the current 'standard' for modern statistical computing hardware?

I am in the market for a new system (probably a laptop) that would be be used primarily for Bayesian/MCMC analyses. If I had unlimited funds I would obviously buy very high end hardware and be done ...
7
votes
2answers
3k views

How to make R's gamm work faster?

Last night I started a complex calculation with gamm() and it took me... user system elapsed 9259.76 326.05 9622.64 (s) ......
4
votes
1answer
1k views

Does it make sense to use PCA when the determinant of the correlation matrix is (almost) zero?

I'm running a PCA over a data set of $N \times p$ size ($N\approx 1000$ being the number of measurements and $p\approx 200$ being the number of dimensions/predictors). I expect many of the predictors ...