I tagged this with every category since I review books in all realms of science.

Though I plan to maintain my Science Shelf archive of book reviews, I will now also publish the reviews on Science Blog.

Adversaries the U.S. currently faces in Iraq rely on surprise and apparent randomness to compensate for their lack of organization, technology and firepower. If one could find some method to their madness, however, the asymmetric threat could be made significantly less serious, according to scientists at The University of Alabama in Huntsville.

Now that the primaries have narrowed the number of viable major-party presidential candidates to three, ScienceDebate2008 promises to give each of them an opportunity to address an issue that is not strictly partisan: science and technology policy and what it means for the future of our nation and the world.

Because the Gaussian distribution is the exponential of squared parameters, the proof of its theorem for adaptation is a rather simple matter, which should be understandable at the high school level. Because the theorem is valid for all Gaussians and all regions of acceptability (even probability functions) it is in principle sufficient to see the proof for a Gaussian with variance = 1 in a single parameter. The proof is easily extended to an arbitrary number of parameters.

Does Michael Crichton wear a tin hat?

Will Kaiser/CDC admit we are infected with this? How will they react in the litterbox?

CDC produces Media Alert - Morgellons

According to Kjellström, 1969, (see reference in the list
http://en.wikipedia.org/wiki/Gaussian_adaptation#References)
a connection between GA and information theory is the average speed of stepwise random walks inside a high dimensional simplex region. It turns out that the speed is asymptotically proportional to (see also the point 7 in blog “Gaussian adaptation as a model of evolution”)
- P log(P),
where P is the probability that a random step will lead to a new feasible position inside the simplex. Maximum speed is obtained when P = 1/e = 0.37.

A plausible interpretation of this is that 1/P is proportional to the time/work needed to find a step leading to a feasible position, while –log(P) is the self-information obtained when such a step may be taken. Thus, – P log(P) may be seen as a measure of efficiency; information divided by the work/time needed to get the information. In addition GA maximizes the average information of a Gaussian distribution

Science Debate 2008 is a nonpartisan effort to promote a public discussion of science and technology policy in the coming U.S. Presidential election.

Supported by numerous university presidents, Nobel Laureates, and other scientific leaders, the effort appears to have reached viability with the announcement of its co-chairs, two congressmen from different political parties.

In the late 60-ties my boss formulated a technical problem: “Try to find system solutions that are insensitive to variations in parameter or component values due to the statistical spread in manufacturing” he said. This means that he wanted the manufacturing yield maximized.