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This estimate is sometimes referred to as the "geometric CV" (GCV), [19] [20] due to its use of the geometric variance. Contrary to the arithmetic standard deviation, the arithmetic coefficient of variation is independent of the arithmetic mean. The parameters μ and σ can be obtained, if the arithmetic mean and the arithmetic variance are known:
The sum of independent geometric random variables with parameter is a negative binomial random variable with parameters and . [14] The geometric distribution is a special case of the negative binomial distribution, with r = 1 {\displaystyle r=1} .
It uses a combination of the energy, momentum, and continuity equations to determine water depth with a given a friction slope (), channel slope (), channel geometry, and also a given flow rate. In practice, this technique is widely used through the computer program HEC-RAS , developed by the US Army Corps of Engineers Hydrologic Engineering ...
A binomial distribution with parameters n = 1 and p is a Bernoulli distribution with parameter p. A negative binomial distribution with parameters n = 1 and p is a geometric distribution with parameter p. A gamma distribution with shape parameter α = 1 and rate parameter β is an exponential distribution with rate parameter β.
Generally, the minimum number of parameters required to describe a model or geometric object is equal to its dimension, and the scope of the parameters—within their allowed ranges—is the parameter space. Though a good set of parameters permits identification of every point in the object space, it may be that, for a given parametrization ...
It is easily measured empirically and can be used to extract certain channels' parameters such as the delay spread. For Small Scale channel modeling, the power delay profile of the channel is found by taking the spatial average of the channel's baseband impulse response i.e. | h b ( t , τ ) | 2 {\displaystyle |h_{b}(t,\tau )|^{2}} over a local ...
Alternatively, the Nakagami distribution (;,) can be generated from the chi distribution with parameter set to and then following it by a scaling transformation of random variables. That is, a Nakagami random variable X {\displaystyle X} is generated by a simple scaling transformation on a chi-distributed random variable Y ∼ χ ( 2 m ...