Multistate and Multinomial Distributions

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The MML estimator for an M-state distribution gives θi=(ni+1/2)/(N+M/2), where ni is the number of observations of statei, during a total of N observations, N=∑i=1..M {ni}.

In general, the uncertainty region for the MML estimate for k-parameters θ = ⟨θ12, ...,θk⟩, is approximately √(12k/F(θ)), where F(θ) is the [Fisher information]. Note that k=M-1 for the multi-state distribution.

3-States, M=3

A 3-state source has two parameters, θ1 and θ2 in [0,1]; i.e., θ=⟨θ12⟩. It is convenient to define θ3, also in [0,1], where θ3=1-θ12, but θ3 is not a third (free) parameter. We observe n1 occurrences of state1, n2 of state2 and n3 of state3 where N=n1+n2+n3. The likelihood, LH = θ1n12n23n3, so ...

-log LH = -n1 log θ1 - n2 log θ2 - n3 log(1-θ12)
d/d θ1 {-log LH} = -n11 + n3/(1-θ12)
d/d θ2 {-log LH} = -n22 + n3/(1-θ12)
d2/d θ12 {-log LH} = n112 + n3/(1-θ12)2
d2/d θ22 {-log LH} = n122 + n3/(1-θ12)2
d2/d θ1 d θ2 {-log LH} = n3/(1-θ12)2 = d2/d θ2 d θ1 {-log LH}

The expection of n1 over the data space is N.θ1, similarly for n2 and n3, so the Fisher is ...

N/θ1+N/θ3 N/θ3 |
N/θ3 N/θ2+N/θ3
(1-θ2)/θ1 1 |
1 (1-θ1)/θ2
= N2
 . {  (1-θ2)
 .  (1-θ1)
  - 1 }
θ32 θ1 θ2
= (N232).((1-θ12)/(θ1 θ2) = N2/(θ123)


It can be shown that for M-states, i.e., M-1 parameters, and probabilities θ1, θ2, ..., θM-1, and θM = 1-θ1-...-θM-1, θ = ⟨θ1,...,θM-1⟩, that F(θ) = NM-1 / (θ12...θM).


Use the HTML FORM below to generate a data sample for specified probabilities and length N. The 'code' button calculates message lengths for various codes. Note, the appoximations used may break down for very small values of N.

Requirements: 1 ≤ M ≤ 10, [1,M]| > 0 (will be normalised), N ≥ 0, and sample ∈ [0, M-1]N.
M= θ= N=


  • C. S. Wallace & D. M. Boulton. An Information Measure for Classification. Computer Journal 11(2) pp185-194, Aug 1968
    (see the appendix) and ...
  • D. M. Boulton & C. S. Wallace. The Information Content of a Multistate Distribution. J. Theor. Biol. 23 pp269-278, 1969.
    When these papers were written there were different notions of the information content of a sequence in use in the literature. W&B showed that if the calculations were done correctly and if all information was truly taken into account then these notions gave essentially the same answer.
And a delightful piece of trivia about dice via Dean McKenzie [7/1999]:
'Several decades ago, the Harvard statistician Frederick Mosteller had an opportunity to test the [dice-tossing] model against the behavior of real dice tossed by a real person. A man named Willard H. Longcor. who had an obsession with throwing dice, came to him with an amazing offer to record the results of millions of tosses. Mosteller accepted, and some time later he received a large crate of big manilla envelopes, each of which contained the results of twenty thousand tosses with a single die and a written summary showing how many runs of different kinds had occurred. "The only way to check the work was by checking the runs and then comparing the results with theory," Mosteller recalls. "It turned out [Longcor] was very accurate" Indeed, the results even highlighted some errors in the then-standard theory of the distribution of runs'.
- Peterson, I. (1998) The Jungles of Randomness. Penguin, London. pp7-8. (originally published 1998 by Wiley, New York).
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