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The memorylessness property asserts that the number of previously failed trials has no effect on the number of future trials needed for a success. Geometric random variables can also be defined as taking values in N 0 {\displaystyle \mathbb {N} _{0}} , which describes the number of failed trials before the first success in a sequence of ...
The term strong Markov property is similar to the Markov property, except that the meaning of "present" is defined in terms of a random variable known as a stopping time. The term Markov assumption is used to describe a model where the Markov property is assumed to hold, such as a hidden Markov model .
The solver is given a grid and a list of words. To solve the puzzle correctly, the solver must find a solution that fits all of the available words into the grid. [1] [2] [8] [9] Generally, these words are listed by number of letters, and further alphabetically. [2] [8] Many times, one word is filled in for the solver to help them begin the ...
This is a known characteristic of the exponential distribution, i.e., its memoryless property. Intuitively, this means that it does not matter how long it has been since the last renewal epoch, the remaining time is still probabilistically the same as in the beginning of the holding time interval.
Taking this one stage further, the clue word can hint at the word or words to be abbreviated rather than giving the word itself. For example: "About" for C or CA (for "circa"), or RE. "Say" for EG, used to mean "for example". More obscure clue words of this variety include: "Model" for T, referring to the Model T.
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A memoryless source is one in which each message is an independent identically distributed random variable, whereas the properties of ergodicity and stationarity impose less restrictive constraints. All such sources are stochastic. These terms are well studied in their own right outside information theory.