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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.
Example of a directed acyclic graph on four vertices. If the network structure of the model is a directed acyclic graph, the model represents a factorization of the joint probability of all random variables. More precisely, if the events are , …, then the joint probability satisfies
The response variable may be non-continuous ("limited" to lie on some subset of the real line). For binary (zero or one) variables, if analysis proceeds with least-squares linear regression, the model is called the linear probability model. Nonlinear models for binary dependent variables include the probit and logit model.
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 next example: 1. B = 3 N. B2 = B 2.
Dead code elimination: If no side effected operation depends on a variable, this variable is considered dead and can be removed. Dynamic graph analytics: GraphBolt [2] and KickStarter [3] capture value dependencies for incremental computing when graph structure changes. Spreadsheet calculators. They need to derive a correct calculation order ...
A statement S2 is input dependent on S1 (written ) if and only if S1 and S2 read the same resource and S1 precedes S2 in execution. The following is an example of an input dependence (RAR: Read-After-Read): S1 y := x + 3 S2 z := x + 5 Here, S2 and S1 both access the variable x. This dependence does not prohibit reordering.
The forgetting curve hypothesizes the decline of memory retention in time. This curve shows how information is lost over time when there is no attempt to retain it. [1] A related concept is the strength of memory that refers to the durability that memory traces in the brain. The stronger the memory, the longer period of time that a person is ...
A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). [1]