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For Minecraft especially, there are websites [1] [2] [non-primary source needed] and articles, [3] [4] dedicated to sharing seeds which have been found to generate interesting maps. The effect of loading a map originally generated in Minecraft 1.6.4 in Minecraft 1.7.2. The map seed is unchanged, but the map generation algorithm has changed ...
As players explore the world, new areas are procedurally generated, using a map seed specified by the player. A new game puts the player in the center of a voxel cube 62 thousand nodes across, so the player can travel 31 thousand nodes in any direction (sideways, up, or down) [ 13 ] before reaching the invisible wall at the end of the world.
A random seed (or seed state, or just seed) is a number (or vector) used to initialize a pseudorandom number generator.. A pseudorandom number generator's number sequence is completely determined by the seed: thus, if a pseudorandom number generator is later reinitialized with the same seed, it will produce the same sequence of numbers.
A randomness extractor, often simply called an "extractor", is a function, which being applied to output from a weak entropy source, together with a short, uniformly random seed, generates a highly random output that appears independent from the source and uniformly distributed. [1] Examples of weakly random sources include radioactive decay or ...
In theoretical computer science and cryptography, a pseudorandom generator (PRG) for a class of statistical tests is a deterministic procedure that maps a random seed to a longer pseudorandom string such that no statistical test in the class can distinguish between the output of the generator and the uniform distribution.
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It can be shown that if is a pseudo-random number generator for the uniform distribution on (,) and if is the CDF of some given probability distribution , then is a pseudo-random number generator for , where : (,) is the percentile of , i.e. ():= {: ()}. Intuitively, an arbitrary distribution can be simulated from a simulation of the standard ...
Random binary trees have been used for analyzing the average-case complexity of data structures based on binary search trees. For this application it is common to use random trees formed by inserting nodes one at a time according to a random permutation. [1] The resulting trees are very likely to have logarithmic depth and logarithmic Strahler ...