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In wireless communication, "the local-mean power expressed in logarithmic values, such as dB or neper, has a normal (i.e., Gaussian) distribution." [83] Also, the random obstruction of radio signals due to large buildings and hills, called shadowing, is often modeled as a log-normal distribution.
In mathematics, addition and subtraction logarithms or Gaussian logarithms can be utilized to find the logarithms of the sum and difference of a pair of values whose logarithms are known, without knowing the values themselves. [1] Their mathematical foundations trace back to Zecchini Leonelli [2] [3] and Carl Friedrich Gauss [4] [1] [5] in the ...
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The sum of probabilities + is a bit more involved to compute in logarithmic space, requiring the computation of one exponent and one logarithm. However, in many applications a multiplication of probabilities (giving the probability of all independent events occurring) is used more often than their addition (giving the probability of at least ...
In probability and statistics, the logarithmic distribution (also known as the logarithmic series distribution or the log-series distribution) is a discrete probability distribution derived from the Maclaurin series expansion
All these extensions are also called normal or Gaussian laws, so a certain ambiguity in names exists. The multivariate normal distribution describes the Gaussian law in the k-dimensional Euclidean space. A vector X ∈ R k is multivariate-normally distributed if any linear combination of its components Σ k j=1 a j X j has a (univariate) normal ...
Every year, celebrities try to capitalize on the holiday season by releasing festive music. Singers like Mariah Carey, Ariana Grande, and Michael Bublé managed to perfect the cheesy art form ...
Here, and are the parameters of the distribution, which are the lower and upper bounds of the support, and is the natural log. The cumulative distribution function is F ( x ; a , b ) = ln ( x ) − ln ( a ) ln ( b ) − ln ( a ) for a ≤ x ≤ b . {\displaystyle F(x;a,b)={\frac {\ln(x)-\ln(a)}{\ln(b)-\ln(a)}}\quad {\text{ for ...