Search results
Results from the WOW.Com Content Network
= is the maximum number [4]: §3 of bits that are in the sequence. The k indicates the size of a unique word of data in the sequence. If you segment the N bits of data into every possible word of length k , you will be able to list every possible combination of 0s and 1s for a k-bit binary word, with the exception of the all-0s word.
The Viterbi algorithm is a dynamic programming algorithm for obtaining the maximum a posteriori probability estimate of the most likely sequence of hidden states—called the Viterbi path—that results in a sequence of observed events.
It takes 3 bits to encode n using straightforward binary encoding, hence 2 3 − n = 8 − 5 = 3 are unused.. In numerical terms, to send a value x, where 0 ≤ x < n, and where there are 2 k ≤ n < 2 k+1 symbols, there are u = 2 k+1 − n unused entries when the alphabet size is rounded up to the nearest power of two.
In cryptography and the theory of computation, the next-bit test [1] is a test against pseudo-random number generators.We say that a sequence of bits passes the next bit test for at any position in the sequence, if any attacker who knows the first bits (but not the seed) cannot predict the (+) st with reasonable computational power.
Just as the shannon describes the maximum possible information capacity of a binary symbol, the hartley describes the information that can be contained in a 10-ary symbol, that is, a digit value in the range 0 to 9 when the a priori probability of each value is 1 / 10 . The conversion factor quoted above is given by log 10 (2).
The probability of erasure is In coding theory and information theory , a binary erasure channel ( BEC ) is a communications channel model. A transmitter sends a bit (a zero or a one), and the receiver either receives the bit correctly, or with some probability P e {\displaystyle P_{e}} receives a message that the bit was not received ("erased") .
The entropy of a binary variable is at most 1 bit, and equality is attained if its probability distribution is uniform. It therefore suffices to exhibit an input distribution that yields a uniform probability distribution for the output . For this, note that it is a property of any binary symmetric channel that a uniform probability ...
In information theory, the source coding theorem (Shannon 1948) [2] informally states that (MacKay 2003, pg. 81, [3] Cover 2006, Chapter 5 [4]): N i.i.d. random variables each with entropy H(X) can be compressed into more than N H(X) bits with negligible risk of information loss, as N → ∞; but conversely, if they are compressed into fewer than N H(X) bits it is virtually certain that ...