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A v-optimal histogram is based on the concept of minimizing a quantity which is called the weighted variance in this context. [1] This is defined as = =, where the histogram consists of J bins or buckets, n j is the number of items contained in the jth bin and where V j is the variance between the values associated with the items in the jth bin.
The bins (intervals) are adjacent and are typically (but not required to be) of equal size. [1] Histograms give a rough sense of the density of the underlying distribution of the data, and often for density estimation: estimating the probability density function of the underlying variable. The total area of a histogram used for probability ...
Bucket sort can be seen as a generalization of counting sort; in fact, if each bucket has size 1 then bucket sort degenerates to counting sort. The variable bucket size of bucket sort allows it to use O(n) memory instead of O(M) memory, where M is the number of distinct values; in exchange, it gives up counting sort's O(n + M) worst-case behavior.
Bucket sort can be mixed with other sorting methods to complete sorting. If it is sorted by bucket sort and insert sort, also is a fairly efficient sorting method. But when the series appears a large deviation from the value: For example, when the maximum value of the series is greater than N times the next largest value.
Arithmetic overflow of the buckets is a problem and the buckets should be sufficiently large to make this case rare. If it does occur then the increment and decrement operations must leave the bucket set to the maximum possible value in order to retain the properties of a Bloom filter. The size of counters is usually 3 or 4 bits.
Power-in-the-bucket and Strehl ratio are two attempts to define beam quality as a function of how much power is delivered to a given area. Unfortunately, there is no standard bucket size (D86 width, Gaussian beam width, Airy disk nulls, etc.) or bucket shape (circular, rectangular, etc.) and there is no standard beam to compare for the Strehl ...
The file structure of a dynamic hashing data structure adapts itself to changes in the size of the file, so expensive periodic file reorganization is avoided. [4] A Linear Hashing file expands by splitting a predetermined bucket into two and shrinks by merging two predetermined buckets into one.
The key size that maps the directory (the global depth), and; The key size that has previously mapped the bucket (the local depth) In order to distinguish the two action cases: Doubling the directory when a bucket becomes full; Creating a new bucket, and re-distributing the entries between the old and the new bucket