Porcine Joy

Spring showers bring pig flowers

Well, maybe not, but I haven’t featured any pigs recently. That could be in violation of the administrivial oink’s website charter.

The Rite of Spring by Igor Stravinsky

Opening measures from the Rite of Spring

The Ides of March are past, and we head into spring. We also approach the one-year anniversary of this blog on March 21 or thereabouts, right in time for the vernal equinox. I would like to take this opportunity to thank my loyal subscribers, all three of you, and my other readers, whether frequent or occasional. Never hesitate to leave comments, especially if it isn’t spam!

Statistics, probability and applied quantitative methods reading recommendations

Finally, I wish acknowledge subject matter experts with nice blogs in my fields of professional interest, including applied probability, (mostly) frequentist methods, and due diligence for purposes of financial and security-focused anomaly detection.

Stats with Cats I don’t like cats, but you can just ignore the photos. This is an accessible, frequently updated blog covering descriptive and inferential statistical methods, mostly explained through charming examples

Data Genetics This blog has excellent graphics (without gratuitous interactive data visualization!) accompanying posts demonstrating statistical, probability and mathematical methods for engineering as applied to a wide range of real world concerns e.g. using Benford’s Law to detect accounting fraud, Hamming Codes for error correction and solving combinatorics problems to demonstrate the poor odds for winning dice and card games.

Error Statistics Philosophy Error statistics quantifies how frequently and reliably different statistical models can be used to detect errors.Error probability statistics uses frequentist error probabilities, not frequentist probability. Frequentist error probability is the relative frequency of errors within a statistical model. Frequentist probability is merely the use of relative frequency of occurrence to infer probability of events. The introductory post, Frequentists in exile acknowledges the long-held perception that only Bayesian methods have respectable statistical foundations. Error Statistics Philosophy focuses on the defensible use of frequentist methods for probability and statistical models, especially in circumstances of limited information and high error avoidance requirements.

Now for a bit of self-promotion…

My Google hobby blog,  In the GooglePlex, is ready to be included on the blogroll here. I was very careful about URL choice. SEO was NOT a consideration! Potential trademark infringement was my concern.

In the GooglePlex is a themed site, unlike this one! Topics are Google-related: product news (both good and bad), trivia, corporate history and humor, whenever there is any to be found. Please feel free to drop by to say hello or ask questions.

Oink of Joy

Any rite of Spring should include an oinker of joy

Published in: on March 20, 2011 at 5:45 am  Comments (1)  
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Economic Models for Turbulent Times Part I

Crust is an algorithm for reconstructing surfaces of any topology. In other words, it is a computational method for digitally rendering any 2-D shape, using data in three-dimensional space as input.

CSAIL buildings at MIT

Strange topology at MIT

Such methods garner a lot of attention these days. Here are a few reasons why: Graphical simulation models are increasingly needed for visualization and testing purposes in the field of particle physics. World of Warcraft and Second Life rely heavily on computationally intensive computer graphics, and scalable distributed systems. The U.S. economy is a highly complex system, partly guided by the results of mathematical models.

Crust was developed as a collaborative effort between two staff scientists at Xerox PARC and a researcher at MIT.

None of this happened recently. In fact, Crust hasn’t been semantically linked with the word “new” since its debut at the 1998 ACM SIGGRAPH Conference.

What is so special about Crust?

The Crust algorithm is special because it has certain features uncommon in most quantitative models, yet highly sought after.

First, Crust offers results with “provable” guarantees. Given a good sample from a smooth surface, Crust’s results are guaranteed. That is, Crust guarantees that its output is topologically correct, converging to the original surface with increasing faithfulness depending on the input data density.

Voronoi pig

Graphical computation with Crust: Voronoi Piggy

The third member of the Crust project team was Manolis Kamvysellis, a Ph.D. student at MIT. Manolis did much of the implementation and testing work—he wrote a short-form version, A New Voronoi Based Reconstruction Algorithm [PDF], of the original ACM journal publication. Happily, he had the good sense to demonstrate Crust with this fine pink pig! Let’s do the same.

Highly efficient porcine reconstruction in three dimensions

Recall that Crust’s criteria for acceptable sample size is determined dynamically . A single topological surface, such as Piggy, may have very detailed surfaces, with high data density. Observe this near Piggy’s ears and snout. Other areas like the hindquarters are quite featureless.

Crust dynamically adjusts its smallest acceptable sample size accordingly. Even minimally detailed surfaces such as Piggy’s lower hind quarters above the hooves can be reconstructed accurately.

To be continued…

Published in: on December 5, 2010 at 7:58 am  Comments (5)  
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Nat’s Little Piggy

Originally uploaded by Nat Nunn Nat says:
This little piglet is only about a week old. They were really scared when I first went in but after a while some came closer to me.
Published in: on April 28, 2010 at 12:24 am  Leave a Comment