Along the same lines as my previous post regarding fashion, statistics are making golf interesting.
Slate's "Moneygolf" series
Seven million shots
How golf really works
The dark art of putting
FYI - "Moneygolf" is a reference to Michael M. Lewis' book "Moneyball", which talks about the field of Sabermetrics (statistics applied to baseball).
FYI2 - After recently watching "The Blind Side" (an emotional drama), I was very surprised at first to find out that it was based on a book by Michael Lewis ("an American contemporary non-fiction author and financial journalist"). However, after finding out that there were two story lines in the book - one about the evolution of NFL offensive strategy and one about Michael Oher - I am once again reminded of the changes that occur when a book is transformed into a movie.
Showing posts with label Statistics. Show all posts
Showing posts with label Statistics. Show all posts
Wednesday, August 11, 2010
Tuesday, February 23, 2010
Compressed Sensing
Article at Wired.com
Compressed sensing works something like this: You’ve got a picture — of a kidney, of the president, doesn’t matter. The picture is made of 1 million pixels. In traditional imaging, that’s a million measurements you have to make. In compressed sensing, you measure only a small fraction — say, 100,000 pixels randomly selected from various parts of the image. From that starting point there is a gigantic, effectively infinite number of ways the remaining 900,000 pixels could be filled in.
The key to finding the single correct representation is a notion called sparsity, a mathematical way of describing an image’s complexity, or lack thereof. A picture made up of a few simple, understandable elements — like solid blocks of color or wiggly lines — is sparse; a screenful of random, chaotic dots is not. It turns out that out of all the bazillion possible reconstructions, the simplest, or sparsest, image is almost always the right one or very close to it.
This question really highlighted the utility of compressed sensing:
Digital cameras, he explains, gather huge amounts of information and then compress the images. But compression, at least if CS is available, is a gigantic waste. If your camera is going to record a vast amount of data only to throw away 90 percent of it when you compress, why not just save battery power and memory and record 90 percent less data in the first place?
Compressed sensing works something like this: You’ve got a picture — of a kidney, of the president, doesn’t matter. The picture is made of 1 million pixels. In traditional imaging, that’s a million measurements you have to make. In compressed sensing, you measure only a small fraction — say, 100,000 pixels randomly selected from various parts of the image. From that starting point there is a gigantic, effectively infinite number of ways the remaining 900,000 pixels could be filled in.
The key to finding the single correct representation is a notion called sparsity, a mathematical way of describing an image’s complexity, or lack thereof. A picture made up of a few simple, understandable elements — like solid blocks of color or wiggly lines — is sparse; a screenful of random, chaotic dots is not. It turns out that out of all the bazillion possible reconstructions, the simplest, or sparsest, image is almost always the right one or very close to it.
This question really highlighted the utility of compressed sensing:
Digital cameras, he explains, gather huge amounts of information and then compress the images. But compression, at least if CS is available, is a gigantic waste. If your camera is going to record a vast amount of data only to throw away 90 percent of it when you compress, why not just save battery power and memory and record 90 percent less data in the first place?
Friday, July 17, 2009
Statistics Resources
Cancer Genomics Tools from Washington University (WUSTL) includes a list of R functions that can be used for various statistical tests.
UCLA's Academic Technology Services department has a page with R links and information. In particular, they have a list of analyses and sample code.
UCLA's Academic Technology Services department has a page with R links and information. In particular, they have a list of analyses and sample code.
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