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Let X be a random sample from a probability distribution with a real non-negative parameter [,). A CLs upper limit for the parameter θ, with confidence level ′, is a statistic (i.e., observable random variable) () which has the property:
In computer science, a generator is a routine that can be used to control the iteration behaviour of a loop.All generators are also iterators. [1] A generator is very similar to a function that returns an array, in that a generator has parameters, can be called, and generates a sequence of values.
For example, the nullable boolean of .NET is specified in PowerShell as [Nullable``1[System.Boolean]]. Python : Prior to version 3.0, backticks were a synonym for the repr() function, which converts its argument to a string suitable for a programmer to view. However, this feature was removed in Python 3.0.
Numeric literals in Python are of the normal sort, e.g. 0, -1, 3.4, 3.5e-8. Python has arbitrary-length integers and automatically increases their storage size as necessary. Prior to Python 3, there were two kinds of integral numbers: traditional fixed size integers and "long" integers of arbitrary size.
The limit of this expression as a → −∞ and b → ∞ does not exist: if the limits are taken so that a = −b, then the limit is zero, while if the constraint 2a = −b is taken, then the limit is ln(2).
Each row shows the state evolving until it repeats. The top row shows a generator with m = 9, a = 2, c = 0, and a seed of 1, which produces a cycle of length 6. The second row is the same generator with a seed of 3, which produces a cycle of length 2. Using a = 4 and c = 1 (bottom row) gives a cycle length of 9 with any seed in [0, 8].
In information theory, the source coding theorem (Shannon 1948) [2] informally states that (MacKay 2003, pg. 81, [3] Cover 2006, Chapter 5 [4]): N i.i.d. random variables each with entropy H(X) can be compressed into more than N H(X) bits with negligible risk of information loss, as N → ∞; but conversely, if they are compressed into fewer than N H(X) bits it is virtually certain that ...
Let X 1 and X 2 be independent realizations of a random variable X. Then X is said to be stable if for any constants a > 0 and b > 0 the random variable aX 1 + bX 2 has the same distribution as cX + d for some constants c > 0 and d. The distribution is said to be strictly stable if this holds with d = 0. [7]