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In econometrics, the seemingly unrelated regressions (SUR) [1]: 306 [2]: 279 [3]: 332 or seemingly unrelated regression equations (SURE) [4] [5]: 2 model, proposed by Arnold Zellner in (1962), is a generalization of a linear regression model that consists of several regression equations, each having its own dependent variable and potentially ...
Flat memory model or linear memory model refers to a memory addressing paradigm in which "memory appears to the program as a single contiguous address space." [1] The CPU can directly (and linearly) address all of the available memory locations without having to resort to any sort of bank switching, memory segmentation or paging schemes.
In 1974, Baddeley and Hitch [5] introduced and made popular the multicomponent model of working memory.This theory proposes a central executive that, among other things, is responsible for directing attention to relevant information, suppressing irrelevant information and inappropriate actions, and for coordinating cognitive processes when more than one task must be done at the same time.
On the x86-64 platform, a total of seven memory models exist, [7] as the majority of symbol references are only 32 bits wide, and if the addresses are known at link time (as opposed to position-independent code). This does not affect the pointers used, which are always flat 64-bit pointers, but only how values that have to be accessed via ...
Region-based memory management works best when the number of regions is relatively small and each contains many objects; programs that contain many sparse regions will exhibit internal fragmentation, leading to wasted memory and a time overhead for region management. Again, in the presence of region inference this problem can be more difficult ...
In operating systems, memory management is the function responsible for managing the computer's primary memory. [1]: 105–208 The memory management function keeps track of the status of each memory location, either allocated or free. It determines how memory is allocated among competing processes, deciding which gets memory, when they receive ...
The running time of an algorithm in the external memory model is defined by the number of reads and writes to memory required. [3] The model was introduced by Alok Aggarwal and Jeffrey Vitter in 1988. [4] The external memory model is related to the cache-oblivious model, but algorithms in the external memory model may know both the block size ...
Memory and learning are created by modifying the 'weights' of the connections between neural units, generally represented as an n×m matrix. The weights are adjusted according to some learning rule or algorithm, such as Hebbian learning. [9] Most of the variety among the models comes from: