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A maximum length sequence (MLS) is a type of pseudorandom binary sequence.. They are bit sequences generated using maximal linear-feedback shift registers and are so called because they are periodic and reproduce every binary sequence (except the zero vector) that can be represented by the shift registers (i.e., for length-m registers they produce a sequence of length 2 m − 1).
defines a variable named array (or assigns a new value to an existing variable with the name array) which is an array consisting of the values 1, 3, 5, 7, and 9. That is, the array starts at 1 (the initial value), increments with each step from the previous value by 2 (the increment value), and stops once it reaches (or is about to exceed) 9 ...
The scope of a name binding is an entire program, which is known as global scope. Variable names with global scope—called global variables—are frequently considered bad practice, at least in some languages, due to the possibility of name collisions and unintentional masking, together with poor modularity, and function scope or block scope ...
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 ...
The "Free Parameter" method is another method (called "snake oil" by H. Wilf) to evaluate these sums. Both methods discussed so far have n as limit in the summation. When n does not appear explicitly in the summation, we may consider n as a "free" parameter and treat s n as a coefficient of F ( z ) = Σ s n z n , change the order of the ...
In probability theory, it is possible to approximate the moments of a function f of a random variable X using Taylor expansions, provided that f is sufficiently differentiable and that the moments of X are finite. A simulation-based alternative to this approximation is the application of Monte Carlo simulations.
In probability theory and statistics, the moment-generating function of a real-valued random variable is an alternative specification of its probability distribution.Thus, it provides the basis of an alternative route to analytical results compared with working directly with probability density functions or cumulative distribution functions.
In such applications, the class describes the randomized algorithm or class of randomized algorithms that one wants to simulate, and the goal is to design an "efficiently computable" pseudorandom generator against whose seed length is as short as possible. If a full derandomization is desired, a completely deterministic simulation proceeds by ...