Hartley (unit)

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Units of
information
Information-theoretic
Data storage
Quantum information

The hartley (symbol Hart), also called a ban, or a dit (short for "decimal digit"),

a priori equiprobability of each possible value. It is named after Ralph Hartley
.

If base 2 logarithms and powers of 2 are used instead, then the unit of information is the shannon or bit, which is the information content of an event if the probability of that event occurring is 1⁄2. Natural logarithms and powers of e define the nat.

One ban corresponds to ln(10)

SI prefix deci-
.

Though there is no associated

IEC 80000-13 of the International Electrotechnical Commission
.

History

The term hartley is named after Ralph Hartley, who suggested in 1928 to measure information using a logarithmic base equal to the number of distinguishable states in its representation, which would be the base 10 for a decimal digit.[5][6]

The ban and the deciban were invented by

Irving John "Jack" Good in 1940, to measure the amount of information that could be deduced by the codebreakers at Bletchley Park using the Banburismus procedure, towards determining each day's unknown setting of the German naval Enigma cipher machine. The name was inspired by the enormous sheets of card, printed in the town of Banbury about 30 miles away, that were used in the process.[7]

Good argued that the sequential summation of decibans to build up a measure of the weight of evidence in favour of a hypothesis, is essentially Bayesian inference.[7] Donald A. Gillies, however, argued the ban is, in effect, the same as Karl Popper's measure of the severity of a test.[8]

Usage as a unit of odds

The deciban is a particularly useful unit for

log-odds, notably as a measure of information in Bayes factors, odds ratios (ratio of odds, so log is difference of log-odds), or weights of evidence. 10 decibans corresponds to odds of 10:1; 20 decibans to 100:1 odds, etc. According to Good, a change in a weight of evidence of 1 deciban (i.e., a change in the odds from evens to about 5:4) is about as finely as humans can reasonably be expected to quantify their degree of belief in a hypothesis.[9]

Odds corresponding to integer decibans can often be well-approximated by simple integer ratios; these are collated below. Value to two decimal places, simple approximation (to within about 5%), with more accurate approximation (to within 1%) if simple one is inaccurate:

decibans exact
value		 approx.
value		 approx.
ratio		 accurate
ratio		 probability
0 100/10 1 1:1 50%
1 101/10 1.26 5:4 56%
2 102/10 1.58 3:2 8:5 61%
3 103/10 2.00 2:1 67%
4 104/10 2.51 5:2 71.5%
5 105/10 3.16 3:1 19:6, 16:5 76%
6 106/10 3.98 4:1 80%
7 107/10 5.01 5:1 83%
8 108/10 6.31 6:1 19:3, 25:4 86%
9 109/10 7.94 8:1 89%
10 1010/10 10 10:1 91%

See also

Notes

  1. ^ This value, approximately 10⁄3, but slightly less, can be understood simply because : 3 decimal digits are slightly less information than 10 binary digits, so 1 decimal digit is slightly less than 10⁄3 binary digits.

References

  1. ISBN 3-11002793-3, 978-3-11002793-8. A reworked and expanded 4th edition
    exists as well.)
  2. ISBN 978-3-11011700-4
    . (320 pages)
  3. LCCN 79-90567
    .
  4. ^ "IEC 80000-13:2008". International Organization for Standardization (ISO). Retrieved 2013-07-21.
  5. Bell System Technical Journal
    . VII (3): 535–563. Retrieved 2008-03-27.
  6. ISBN 0-486-68210-2
    .
  7. ^
    MR 0548210
    .
  8. MR 0055678
    .
  9. Good, Irving John (1985). "Weight of Evidence: A Brief Survey"
    (PDF). Bayesian Statistics. 2: 253. Retrieved 2012-12-13.
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