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In computational complexity theory, DTIME (or TIME) is the computational resource of computation time for a deterministic Turing machine. It represents the amount of time (or number of computation steps) that a "normal" physical computer would take to solve a certain computational problem using a certain algorithm .
The Silver–Meal heuristic is a production planning method in manufacturing, composed in 1973 [1] by Edward A. Silver and H.C. Meal. Its purpose is to determine production quantities to meet the requirement of operations at minimum cost.
Example 3: Bounded normal mean: When estimating the mean of a normal vector (,), where it is known that ‖ ‖. The Bayes estimator with respect to a prior which is uniformly distributed on the edge of the bounding sphere is known to be minimax whenever M ≤ n {\displaystyle M\leq n\,\!} .
Time Hierarchy Theorem. If f(n) is a time-constructible function, then there exists a decision problem which cannot be solved in worst-case deterministic time o(f(n)) but can be solved in worst-case deterministic time O(f(n)log f(n)).
In computational complexity theory, DLOGTIME is the complexity class of all computational problems solvable in a logarithmic amount of computation time on a deterministic Turing machine. It must be defined on a random-access Turing machine, since otherwise the input tape is longer than the range of cells that can be accessed by the machine. It ...
expected time: the best estimate of the time required to accomplish an activity (te) or a path (TE), accounting for the fact that things don't always proceed as normal (the implication being that the expected time is the average time the task would require if the task were repeated on a number of occasions over an extended period of time).
where τ is the mean service time; σ 2 is the variance of service time; and ρ=λτ < 1, λ being the arrival rate of the customers. For M/M/1 queue, the service times are exponentially distributed, then σ 2 = τ 2 and the mean waiting time in the queue denoted by W M is given by the following equation: [ 5 ]
Similar techniques can change from a discrete, stochastic description to a deterministic, continuum description in a time-and space dependent manner. [21] The use of this technique enables the capturing of noise due to small copy numbers, while being much faster to simulate than the conventional Gillespie algorithm.
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