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This is an accepted version of this page This is the latest accepted revision, reviewed on 17 January 2025. Observation that in many real-life datasets, the leading digit is likely to be small For the unrelated adage, see Benford's law of controversy. The distribution of first digits, according to Benford's law. Each bar represents a digit, and the height of the bar is the percentage of ...
The efficiency of accessing a key depends on the length of its list. If we use a single hash function which selects locations with uniform probability, with high probability the longest chain has ( ) keys. A possible improvement is to use two hash functions, and put each new key in the shorter of the two lists.
The probability of this happening is 1 in 13,983,816. The chance of winning can be demonstrated as follows: The first number drawn has a 1 in 49 chance of matching. When the draw comes to the second number, there are now only 48 balls left in the bag, because the balls are drawn without replacement .
The host always reveals a goat and always offers a switch. If and only if he has a choice, he chooses the leftmost goat with probability p (which may depend on the player's initial choice) and the rightmost door with probability q = 1 − p. [38] [34] If the host opens the rightmost ( P=1/3 + q/3 ) door, switching wins with probability 1/(1+q).
Digit sums and digital roots can be used for quick divisibility tests: a natural number is divisible by 3 or 9 if and only if its digit sum (or digital root) is divisible by 3 or 9, respectively. For divisibility by 9, this test is called the rule of nines and is the basis of the casting out nines technique for checking calculations.
Additive smoothing is a type of shrinkage estimator, as the resulting estimate will be between the empirical probability (relative frequency) / and the uniform probability /. Invoking Laplace's rule of succession , some authors have argued [ citation needed ] that α should be 1 (in which case the term add-one smoothing [ 2 ] [ 3 ] is also used ...
Let n be very large and consider a random graph G on n vertices, where every edge in G exists with probability p = n 1/g −1. We show that with positive probability, G satisfies the following two properties: Property 1. G contains at most n/2 cycles of length less than g. Proof. Let X be the number cycles of length less than g.
In probability theory and computer science, a log probability is simply a logarithm of a probability. [1] The use of log probabilities means representing probabilities on a logarithmic scale ( − ∞ , 0 ] {\displaystyle (-\infty ,0]} , instead of the standard [ 0 , 1 ] {\displaystyle [0,1]} unit interval .