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The opposite, a graph with only a few edges, is a sparse graph. The distinction of what constitutes a dense or sparse graph is ill-defined, and is often represented by 'roughly equal to' statements. Due to this, the way that density is defined often depends on the context of the problem.
The density of the maximally dense subgraph of a graph is sometimes referred to as its subgraph density. A subgraph with maximal density can also be seen as a subgraph with maximal average degree in the graph. Subgraph density is asymptotic to the related notion of arboricity and to graph degeneracy.
A stronger notion is polynomial expansion, meaning that the function f used to bound the edge density of shallow minors is a polynomial.If a hereditary graph family obeys a separator theorem, stating that any n-vertex graph in the family can be split into pieces with at most n/2 vertices by the removal of O(n c) vertices for some constant c < 1, then that family necessarily has polynomial ...
In probability theory and statistics, the beta distribution is a family of continuous probability distributions defined on the interval [0, 1] or (0, 1) in terms of two positive parameters, denoted by alpha (α) and beta (β), that appear as exponents of the variable and its complement to 1, respectively, and control the shape of the distribution.
The correlated variation of a kernel density estimate is very difficult to describe mathematically, while it is simple for a histogram where each bin varies independently. An alternative to kernel density estimation is the average shifted histogram, [8] which is fast to compute and gives a smooth curve estimate of the density without using kernels.
Figure 1: The left graph shows a probability density function. The right graph shows the cumulative distribution function. The value at a in the cumulative distribution equals the area under the probability density curve up to the point a. Absolutely continuous probability distributions can be described in several ways.
Sigmoid curves are also common in statistics as cumulative distribution functions (which go from 0 to 1), such as the integrals of the logistic density, the normal density, and Student's t probability density functions. The logistic sigmoid function is invertible, and its inverse is the logit function.
In the study of traffic flow theory, the flow-density diagram is used to determine the traffic state of a roadway. Currently, there are two types of flow density graphs: parabolic and triangular. Academia views the triangular flow-density curve as more the accurate representation of real world events. The triangular curve consists of two vectors.
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