The Gaussian Free Field (GFF) of dimension d is a gaussian stochastic process $(X_v, v \in B_N)$ where the variables are indexed by the vertices of a box $B_N \in \mathbb{Z}^d$. The covariance matrix is given by the Green's function of the discrete Laplace operator on $B_N$ with Dirichlet boundary conditions.
Takayuki Kawada has written: 'Asymptotic behavior of the maxima over high levels for a homogenous Gaussian random fields' -- subject(s): Asymptotic expansions, Gaussian processes, Maxima and minima, Random fields
The modes of a probability density function might be defined as the (countable) set of points in the domain of the function for which the function achieves local maxima. Since the probability density function for the uniform distribution is constant by definition it has no local maxima, hence no modes. Hence, it cannot be bimodal.
No it is not the same. The power distribution center is like a big fues box.
You are likely familiar with the probability density function of the normal distribution--that is, the bell-shaped curve.A bimodal distribution is one whose probability density function has two 'humps' or maxima. In other words, values of the random variable are more likely to occur around where those two maxima occur than elsewhere, in the same way that values of a normally distributed random variable are more likely to occur around its maximum.
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Maxima mean...(put definition) its a way to put maxima in a sentence or what does maxima mean?
The Maxima is a Nissan model
no
no
Look at 'where is the starter in a 1998 Nissan Maxima';
No. A distribution may be non-skewed and bimodal or skewed and bimodal. Bimodal means that the distribution has two modes, or two local maxima on the curve. Visually, one can see two peaks on the distribution curve. Mixture problems (combination of two random variables with different modes) can produce bimodal curves. See: http://en.wikipedia.org/wiki/Bimodal_distribution A distribution is skewed when the mean and median are different values. A distribution is negatively skewed when the mean is less than the median and positively skewed if the mean is greater than the median. See: http://en.wikipedia.org/wiki/Skewness
no