eemax

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In the US, the rule used to be first to file, but is now first to invent (along with the rest of the world) since the enactment of the America Invents Act in 2013.

First to file/invent isn't actually relevant for prior art though. Any publicly available source that describes the invention can serve as prior art. However, patent lawyers are very good at "writing around" existing patents and other prior art.

The OP glosses over this a bit, but it's mentioned in the footnotes: the "hats" represent a commitment scheme, which is a way for the prover commit to a specific color assignment, before revealing what that assignment actually is.

When the verifying party chooses the two vertices he wants to see, the prover can reveal only those two, and the verifier actually has two things to check:

1) the colors revealed are actually two different colors (otherwise the purported solution obviously isn't correct)

2) the colors revealed are consistent with the prover's overall commitment. (in other words, that the prover didn't change his answer "on-the-fly")

You basically have the right idea, well funded in this context means that the company has drawn a lot of investment capital. Companies without investor capital have to generate enough revenue per employee to actually pay for the cost of doing business.

Start ups which are growing quickly can operate at a loss, supported by venture capital, and thus may have lower revenue per employee.

If you dismiss any positive results in parapsychology as false on reductionist grounds, the whole field becomes a sort of meta-control group for statistics that are measuring real things.

http://lesswrong.com/lw/1ib/parapsychology_the_control_group...

Anyways, this article singles our Turing and Kahneman but it seems this is a fairly common pitfall/mistake among, well, almost everyone who uses statistics.

Are there really people other than philosophers who are actually "Bayesians" or "Frequentists"?

Frequentist statistics are just a special case of Bayesian statistics with certain implicit, built-in priors - if you pick these priors using Bayesian statistics you'll get the same answers.

The frequentest toolbox is just a collection of these useful special cases of Bayesian statistics with priors that usually make sense in practice. The advantage is this greatly simplifies the statistical analysis for many problems. Sometimes these methods fail though, when the priors they rely on implicitly are far off from the actual, and so a Bayesian analysis is needed.

Of course, sometimes it's difficult or impossible to use Bayesian methods.

It's analogous to classical vs. quantum/relativistic physics. For many cases, you can get the right answer to a problem using classical physics. But under certain conditions, classical physics breaks down, and you must apply quantum or relativistic physics to get a meaningful answer. On the other hand, for many problems it would be silly or impossible to use quantum or relativistic physics because classical is perfectly good.

So you might get into argument about whether a specific case can be adequately handled by frequentest statistical methods, or whether a bayesian analysis can/must be applied.

The philosophical debate about the different approaches to the nature of probability is just that - a philosophical one, and one that has no real bearing on the usefulness and correctness of bayesian or frequentist statistics in practice.