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seed is a type of reference table used in dbt for static or infrequently changed data, like for example country codes or lookup tables), which are CSV based and typically stored in a seeds folder. References
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 pseudorandom binary sequence (PRBS), pseudorandom binary code or pseudorandom bitstream is a binary sequence that, while generated with a deterministic algorithm, is difficult to predict [1] and exhibits statistical behavior similar to a truly random sequence.
R. Couture and P. L’Ecuyer [24] Mersenne Twister (MT) 1998 M. Matsumoto and T. Nishimura [25] Closely related with LFSRs. In its MT19937 implementation is probably the most commonly used modern PRNG. Default generator in R and the Python language starting from version 2.3. Xorshift: 2003 G. Marsaglia [26] It is a very fast sub-type of LFSR ...
The list of widely used generators that should be discarded is much longer [than the list of good generators]. Do not trust blindly the software vendors. Check the default RNG of your favorite software and be ready to replace it if needed. This last recommendation has been made over and over again over the past 40 years.
While these runs of zero are easy to detect, they occur too frequently for this method to be of practical use. The middle-squared method can also get stuck on a number other than zero. For n = 4, this occurs with the values 0100, 2500, 3792, and 7600. Other seed values form very short repeating cycles, e.g., 0540 → 2916 → 5030 → 3009.
Intuitively, an extractor takes a weakly random n-bit input and a short, uniformly random seed and produces an m-bit output that looks uniformly random. The aim is to have a low d {\displaystyle d} (i.e. to use as little uniform randomness as possible) and as high an m {\displaystyle m} as possible (i.e. to get out as many close-to-random bits ...
Noam Nisan (1992) showed that this derandomization can actually be achieved with a pseudorandom generator of seed length () that fools all ()-space machines. Nisan's generator has been used by Saks and Zhou (1999) to show that probabilistic log-space computation can be simulated deterministically in space O ( log 1.5 n ...