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Technical Analysis Using Multiple Timeframes - Understanding by Brian Shannon

By Brian Shannon

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Extra resources for Technical Analysis Using Multiple Timeframes - Understanding Market Structure and Profit from Trend Alignment

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Is called stationary if for any integer r the distribution of the random vector (Xt+1 , . . , Xt+r ) does not depend on the shift parameter t (cf. 4). A sequence X1 , X2 , . . is said to be q-dependent if the random vectors (Xa , . . , Xb ) and (Xc , . . , Xd ) are independent whenever c − b > q. The term N(μ , σ 2 ) denotes the normal distribution with mean μ and variance σ 2 . 4. 2 as strictly stationary to distinguish them from weakly stationary (aka weak-sense stationary or wide-sense stationary) stochastic processes.

The das random numbers may be algorithmically postprocessed to internal random numbers in order to reduce potential weaknesses. , it may only depend on the current das random numbers, or 3 Evaluation Criteria for Physical Random Number Generators 27 Fig. 1 Generic design of a physical RNG. combine the current das random numbers with memory values that depend on the preceding das random numbers (and maybe some other, possibly secret parameters). Strong noise sources do not necessarily require algorithmic postprocessing.

11) If the random variables X1 , . . , describing a memoryless random source) for large n we have log2 (wα (X1 , . . 3). Note that for the uniform distribution on Ω we have H1 = log2 (k) = H∞ , and in the vicinity of the uniform distribution H1 and H∞ give similar values. In the context of PTRNGs, we are usually faced with stationary processes that assume values in Ω = {0, 1}, for which the entropy per random bit is close to 1. Usually, these processes only have a short-range memory and/or are rapidly mixing.

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