@inproceedings{36571197d9d64f259eb922d235109394,

title = "MML estimation of the parameters of the spherical fisher distribution",

abstract = "The information-theoretic Minimum Message Length (MML) principle leads to a general invariant Bayesian technique for point estimation. We apply MML to the problem of estimating the concentration parameter, n, of spherical Fisher distributions. (Assuming a uniform prior on the field direction, μ, MML simply returns the Maximum Likelihood estimate for μ.) In earlier work, we dealt with the yon Mises circular case, d = 2. We say something about the general case for arbitrary d ≥ 2 and how to derive the MML estimator, but here we only carry out a complete calculation for the spherical distribution~ with d = 3. Our simulation results show that the MML estimator compares very favourably against the classical methods of Maximum Likelihood and marginal Maximum Likelihood (R.A. Fisher (1953), Schou (1978)). Our simulation results also show that the MML estimator compares quite favourably against alternative Bayesian methods.",

author = "Dowe, {David L.} and Oliver, {Jonathan J.} and Wallace, {Chris S.}",

year = "1996",

language = "English",

isbn = "3540618635",

series = "Lecture Notes in Computer Science ",

publisher = "Springer",

pages = "213--227",

editor = "Setsuo Arikawa and Sharma, {Arun K.}",

booktitle = "Algorithmic Learning Theory - 7th International Workshop, ALT 1996, Proceedings",

note = "7th International Workshop on Algorithmic Learning Theory, ALT 1996 ; Conference date: 23-10-1996 Through 25-10-1996",

}