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Topic "High dimensional" was discussed 85 times on 34 sites in last 3 months

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high dimensional was discussed on the following sites:

	Wilmott \| Serving The Quantitative Finance... - 26
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	Physics Help and Math Help - Physics Forums - 4
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Related threads on high dimensional:

	High DImensional IBVPs and Seperation of variables Technique High DImensional IBVPs and Seperation of variables Technique
	Why does high-dimensional LDS work? Why does high-dimensional LDS work?
	capture the “essential” aspects of a... capture the “essential” aspects of a high-dimensional object using as few measurements as possible. 11:14 PM Aug 23rd from web
	Random Matrices: High Dimensional Phenomena (London... Random Matrices: High Dimensional Phenomena (London Mathematical Society Lecture Note Series) - by Gordon Blower. http://j.mp/1LcSae

Latest threads on high dimensional:

Random Matrices: High Dimensional Phenomena (London Mathematical Society Lecture Note Series) - by Gordon Blower. http://j.mp/1LcSae

Started 2 days, 22 hours ago (2009-11-10 07:21:34) by newbook

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Multivariate Statistics: High-Dimensional and Large-Sample Approximations (Wiley Series… - by Yasunori Fujikoshi et al. http://bit.ly/4wO550

Started 2 weeks, 3 days ago (2009-10-26 10:57:00) by newbook

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Why does high-dimensional LDS work?

Started 1 month ago (2009-10-13 08:56:00) by outrun

Using low discrepancy sequences to (e.g.) Monte Carlo integrate a 300 dimensional payoff function, turns out to give better performance that using non-LDS sequences. In my opinion this has nothing to do with the LDS property, laying out the most minimalistic grid of 2x2x... (300-times) grid will need 2E90 points. Using 'only' a trillion Monte Carlo runs doesn't sample ...

Source: Wilmott | Serving The Quantitative Finance Communi...

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RE: Why does high-dimensional LDS work?

Started 4 weeks, 1 day ago (2009-10-14 22:29:00) by Traden4Alpha

Obviously, the splitting criterion should be an estimate of the total integration error across that subspace. Ceteris paribus, we should see that small, densely sampled subspaces have lower expected integration error and thus the algorithm should tend to split larger subspaces, first. This process should be naturally self-limiting because with each split, the estimated ...

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