These days the fingerprint could easily consist of millions of variations. You'd have no chance of framing someone without a sample of their DNA. If you had a sample, there would be no point in synthesizing it.

There's hope it can be brought down to a few hundred dollars soon, but sequencing a new human genome currently costs several thousand dollars.

Can't speak for the east coast, but have you ever been to Seattle or San Francisco? You'll find many bars with excellent selections of microbrews on tap. These breweries have no long history of trial and error, and it has nothing to do with prohibition; many are less than 20 years old! They are young because microbrews have only recently become trendy.

I believe that many of these quantities still are tabled. Computers are getting quicker, but data sets are growing even more rapidly and these approximations still matter. There are many good methods that don't find use because the answers can't be tabled and take too long to compute through permutation or simulation.

and this is an even less round about way of saying "I'm a douche." He made some good points.

For restricted areas, the bus system in Seattle isn't too bad. If you work downtown or near one of the main roads, the bus is preferable to driving (because parking is a pita). You could choose a house or apartment near a bus stop., but most of us can't be picky about where we work, so if that's not near one then you're SOL anyway.

Much research in statistics is focused on very applied problems in computational biology. You are right that statisticians do not perform the experiments; it's unrealistic to expect them to have the lab experience necessary. The mathematical statisticians are working on problems such as multiple testing (in many studies there are hundreds of thousands of hypotheses being tested) and inferences of very high dimensional data. These are relevant topics and the work is motivated by the recent shift in the types of data we collect in experiments.

Some interesting subsets of computer science (machine learning and AI) are now focused on statistical models. You have pointed out that much statistical work isn't developed by statisticians. That doesn't mean the field is dead -- it is thriving!

Some Bayesians who rely on MCMC and may not care as much, but generally it is still important to find models with closed form likelihoods and optimization updates. Applied statisticians work hard to keep things in closed form precisely because it matters in practice (but not in theory).

Your Story Is Somewhat Humorous But I Did Not Laugh?

Thank you for saying what so many are afraid to (for fear of offtopic mod)

> This GCV is used in Q-value routine of 2003 to determine false discovery rates by smoothing over the P-values of thousands of genes

Are you referring to this paper ?

http://www.genomine.org/papers/Storey_Tibs_PNAS_2003.pdf

The authors didn't mention any numerical difficulties. Maybe it would have been worth contacting one of them for help--assuming you're not one of them.

Last minute editing of ppt presentations and easily displaying through a room's digital project are two things that would push me towards a windows netbook.

I do not agree that we are good at evaluating teachers. 90% of a student's opinion of a teacher is based on the grades they receive. Another 5% is simply arbitrary--disliking mannerisms and such. One of twenty times it might be a useful judgment.

I saw no mention of the EPA in the article.

However, were it not a troll, the "Try again." comment would bother me. Someone may have little to contribute to the thread except for a link to a journal article. Let them post it! It still gives information to a non-negligible subset of the slashdot readership.

So what does google get out of this?

Yeah, you'll get a few chuckles out of this

http://autos.msn.com/advice/CRArt.aspx?contentid=4023544

The American cars are apparently more reliable than you remember.