It is all about sharing index space. The amount of space is that needed so each segment of the space all account for -iLog(i), as that is the logarithm of their peak equalizes information entropy for the various quantities. These Huffman trees are stable Poisson points when N goes to infinity.
Closed Bayesian a, quotient ring, it does Lie groups. But it counts on hyperbolic surface and indexes are hyperbolic angles.The model is the least redundant, you have taken all combinations and reduced them. Trying to latch on to some finite set of Markov solutions that yield minimal deviation counts.
Does this work for currency pricing?
Well, yes, but the currency traders already do this, they have a value added net. It is short, that is the thing, the pros already have things well hedged.
Does it work for the stock market?
Yes, it gives an optimum ratio in a somewhat closed set of prices. So this is something Mr. Buffet might use. Transportation price pairs, over region, how many regions. Then you have a measure of relative stability and can look more intelligently at an individual company.
Works for epidemics, consumer possibilities. The key is getting the closed set of matching dollar pairs. Walmart is the classic case.
In the pit?
ll it means is that the pit boss will be taking some sample space, and it is bound by contract; when the bots do it. The it boss is x, and in the simple case take x=1. We are not doing much more than nine bit accuracy on this bid matching. Pit boss should carry a bit less than one percent of flow.
No comments:
Post a Comment