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Most macros viruses are stand-alone; they do not depend on other macros (for the infectious part of the virus, not for the replication for some viruses), but some macros viruses do. They are called parasitic macros. [10]: 614–615 When launched, they check other macros (viruses or not), and append their contents to them. In this way, all of ...
The Macro Recorder records actions of the user and generates VBA code in the form of a macro. These actions can then be repeated automatically by running the macro. The macros can also be linked to different trigger types like keyboard shortcuts, a command button or a graphic.
Random number generators are important in many kinds of technical applications, including physics, engineering or mathematical computer studies (e.g., Monte Carlo simulations), cryptography and gambling (on game servers). This list includes many common types, regardless of quality or applicability to a given use case.
A parameterized macro is a macro that is able to insert given objects into its expansion. This gives the macro some of the power of a function. As a simple example, in the C programming language, this is a typical macro that is not a parameterized macro, i.e., a parameterless macro: #define PI 3.14159
Randomization is a statistical process in which a random mechanism is employed to select a sample from a population or assign subjects to different groups. [1] [2] [3] The process is crucial in ensuring the random allocation of experimental units or treatment protocols, thereby minimizing selection bias and enhancing the statistical validity. [4]
For example, if a teacher has a class arranged in 5 rows of 6 columns and she wants to take a random sample of 5 students she might pick one of the 6 columns at random. This would be an epsem sample but not all subsets of 5 pupils are equally likely here, as only the subsets that are arranged as a single column are eligible for selection.
A ubiquitous use of unpredictable random numbers is in cryptography, which underlies most of the schemes which attempt to provide security in modern communications (e.g., confidentiality, authentication, electronic commerce, etc.). For example, if a user wants to use an encryption algorithm, it is best that they select a random number as the key.
Rejection sampling is based on the observation that to sample a random variable in one dimension, one can perform a uniformly random sampling of the two-dimensional Cartesian graph, and keep the samples in the region under the graph of its density function. [1] [2] [3] Note that this property can be extended to N-dimension functions.