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Included are diagram techniques, chart techniques, plot techniques, and other forms of visualization. There is also a list of computer graphics and descriptive geometry topics . Simple displays
An example of a roofline model with added bandwidth ceilings. In this model, the two additional ceilings represent the absence of software prefetching and NUMA organization of memory . An example roofline model with added in-core ceilings , where the two added ceilings represent the lack of instruction level parallelism and task level parallelism .
In computer science, a graph is an abstract data type that is meant to implement the undirected graph and directed graph concepts from the field of graph theory within mathematics. A graph data structure consists of a finite (and possibly mutable) set of vertices (also called nodes or points ), together with a set of unordered pairs of these ...
In mathematics and computer science, graph theory is the study of graphs, which are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of vertices (also called nodes or points ) which are connected by edges (also called arcs , links or lines ).
A line rendered in this way exhibits some special properties that may be taken advantage of. For example, in cases like this, sections of the line are periodical. This results in an algorithm which is significantly faster than precise variants, especially for longer lines. A worsening in quality is only visible on lines with very low steepness.
The graphs can be used together to determine the economic equilibrium (essentially, to solve an equation). Simple graph used for reading values: the bell-shaped normal or Gaussian probability distribution, from which, for example, the probability of a man's height being in a specified range can be derived, given data for the adult male population.
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning.
A configuration graph is a directed labeled graph where the label of the vertices are the possible configurations of the models and where there is an edge from one configuration to another if it corresponds to a computational step of the model. [citation needed]