http://prl.aps.org/accepted/L/0707aY...6bf3f1fa9e8bc6
http://news.sciencemag.org/scienceno...s-is-hard.html
That makes it doubly difficult for AGW theorists because they use the AR(1) model (first order autoregression), a time-series modeling tool, with the assumption the next year's temperature is dependent on this year's temperature, but not on any of the previous years. Previous years data, even paleodata, is collected so that the assumption, among many others, can be applied. The goal of regression is to select the parameters of the model so as to minimize the sum of the squared residuals. This is referred to as ordinary least squares (OLS) estimation and results in best linear unbiased estimates (BLUE) of the parameters if and only if the Gauss-Markov assumptions are satisfied, i.e., all errors have the same variance, and any two different values of the error term are drawn from "uncorrelated" distributions in which coefficients are not allowed to depend on the underlying coefficients which are not unobservable, hence the term "synthetic".
It raises the question many have asked before: just what are those computer models that project dynamic atmospheric behavior 50 years into the future actually computing? After I read the HARRY_README.TXT file it had me wondering as well. But, when I read the 1,072 emails that the whistle blower released I understood what the game was.
Extracting dynamical equations from experimental data is NP hard
Toby S. Cubitt, Jens Eisert, and Michael M. Wolf
Accepted Thursday Feb 16, 2012
The behavior of any physical system is governed by its underlying dynamical equations. Much of physics is concerned with discovering these dynamical equations and understanding their consequences. In this work, we show that, remarkably, identifying the underlying dynamical equation from any amount of experimental data, however precise, is a provably computationally hard problem (it is NP-hard), both for classical and quantum mechanical systems. As a by-product of this work, we give complexity-theoretic answers to both the quantum and classical embedding problems, two long-standing open problems in mathematics (the classical problem, in particular, dating back over 70 years).
Toby S. Cubitt, Jens Eisert, and Michael M. Wolf
Accepted Thursday Feb 16, 2012
The behavior of any physical system is governed by its underlying dynamical equations. Much of physics is concerned with discovering these dynamical equations and understanding their consequences. In this work, we show that, remarkably, identifying the underlying dynamical equation from any amount of experimental data, however precise, is a provably computationally hard problem (it is NP-hard), both for classical and quantum mechanical systems. As a by-product of this work, we give complexity-theoretic answers to both the quantum and classical embedding problems, two long-standing open problems in mathematics (the classical problem, in particular, dating back over 70 years).
Mathematicians recognize a set of truly hard problems that can't be simplified, Cubitt explains. They also know that these problems are all variations of one another. By showing that the challenge of turning physics data into equations is actually one of those problems in disguise, the team showed this task is also truly hard. As a result, any general algorithm that turns a data set into a formula that describes the system over time can't be simplified so that it can run on a computer, the team reports in an upcoming issue of Physical Review Letters.
It raises the question many have asked before: just what are those computer models that project dynamic atmospheric behavior 50 years into the future actually computing? After I read the HARRY_README.TXT file it had me wondering as well. But, when I read the 1,072 emails that the whistle blower released I understood what the game was.
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