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1. B = 3 2. A = B + 1 3. B = 7 Example: MUL R3,R1,R2 ADD R2,R5,R6 It is clear that there is anti-dependence between these 2 instructions. At first we read R2 then in second instruction we are Writing a new value for it. An anti-dependency is an example of a name dependency. That is, renaming of variables could remove the dependency, as in the ...
dependence – an adaptive state associated with a withdrawal syndrome upon cessation of repeated exposure to a stimulus (e.g., drug intake) drug sensitization or reverse tolerance – the escalating effect of a drug resulting from repeated administration at a given dose; drug withdrawal – symptoms that occur upon cessation of repeated drug use
The covariance is sometimes called a measure of "linear dependence" between the two random variables. That does not mean the same thing as in the context of linear algebra (see linear dependence ). When the covariance is normalized, one obtains the Pearson correlation coefficient , which gives the goodness of the fit for the best possible ...
[6] [7] It is possible to have multiple independent variables or multiple dependent variables. For instance, in multivariable calculus, one often encounters functions of the form z = f(x,y), where z is a dependent variable and x and y are independent variables. [8] Functions with multiple outputs are often referred to as vector-valued functions.
If is a non-empty set with a dependence relation , then always has a basis with respect to . Furthermore, any two bases of X {\displaystyle X} have the same cardinality . If a S {\displaystyle a\triangleleft S} and S ⊆ T {\displaystyle S\subseteq T} , then a T {\displaystyle a\triangleleft T} , using property 3. and 1.
In a vector space, the additive inverse −v (often called the opposite vector of v) has the same magnitude as v and but the opposite direction. [11] In modular arithmetic, the modular additive inverse of x is the number a such that a + x ≡ 0 (mod n) and always exists. For example, the inverse of 3 modulo 11 is 8, as 3 + 8 ≡ 0 (mod 11). [12]
A value of 0.5 indicates the absence of long-range dependence. [8] The closer H is to 1, the greater the degree of persistence or long-range dependence. H less than 0.5 corresponds to anti-persistency, which as the opposite of LRD indicates strong negative correlation so that the process fluctuates violently.
The equivalence closure of is denoted or (,,) and called (,,)-equivalence. Informally, p ≡ q {\displaystyle p\equiv q} holds if the string p {\displaystyle p} can be transformed into q {\displaystyle q} by a finite sequence of swaps of adjacent independent symbols.