Snob page
Welcome to
David Dowe's
Snob page
(for
MML
finite
mixture models).
(See the page re-formatted and perhaps out of date
here.)
Papers on theory behind Snob
and
papers on applications of Snob.
Snob uses the
Minimum Message Length
(MML)
principle to do
mixture modelling to infer
MML
finite mixture models.
Mixture modelling (or mixture modeling) concerns modelling a statistical
distribution by a mixture (or weighted sum) of other distributions.
Mixture modelling is also known as
unsupervised concept learning or unsupervised learning
(in Artificial Intelligence)
intrinsic classification (in Philosophy), or, classification
clustering
numerical taxonomy
Name of program:
As per sec. 0.2.4, p535, footnote 113 in
D. L. Dowe (2008),
"Foreword
re C. S. Wallace",
Computer Journal,
Vol. 51, No. 5
(Sept. 2008)
[Christopher
Stewart WALLACE (1933-2004) memorial special issue],
pp523-560
(and
here),
the program was given the name ``Snob'' because of Chris Wallace's
self-effacing joke that it (supposedly) makes ``arbitrary class distinctions''.
Minimum Message Length
(MML)
is a method of machine learning, statistical
inference, inductive learning, "knowledge discovery" and "data mining" very
much in line with the notions of
Kolmogorov
complexity
and
algorithmic information theory
pioneered by
R. J. Solomonoff,
A. N. Kolmogorov and
Greg Chaitin.
See also
Wallace
& Dowe (1999a),
"Minimum
Message Length and Kolmogorov complexity",
Comp.
J., Vol 42, No. 4,
270-283
[which is the
Computer Journal's
most downloaded ``full text as .pdf'' article -
see, e.g.,
here].
[Possibly see also Ray Solomonoff (1926-2009)
85th memorial
conference (Wedn 30 Nov - Fri 2 Dec 2011),
1st
Call for Papers
and
conference
proceedings.]
The original Snob paper was:
Wallace, C. S.
and Boulton, D.M. (1968),
`An Information Measure for Classification',
Computer Journal,
Vol. 11,
No. 2, 1968,
pp.
185-194.
This is the same paper in which
MML
was developed.
(See also more recent Snob
theory
and
application
papers.)
See also
D. L. Dowe (2008),
"Foreword
re C. S. Wallace",
Computer Journal,
Vol. 51, No. 5
(Sept. 2008)
[Christopher
Stewart WALLACE (1933-2004) memorial special issue],
pp523-560
(and
here)
for a survey of all of Wallace's
works - including all his MML work
and his Snob MML mixture modelling work.
Snob currently deals with
finite mixture models
(or a finite mixture model) of
Normal (or Gaussian) distributions
discrete multi-state (also called Bernoulli or categorical) distributions
Poisson distributions
von Mises circular distributions
missing data
Chris Wallace
extended Snob in
1998 to deal with spatial correlation
(and Markov fields),
as occurs in images.
Gerhard Visser and David Dowe
(2007,
2009)
endeavoured to extend
Wallace (1998, above)
with "G. Visser
and D. Dowe" (2007)
"Minimum Message
Length Clustering Of Spatially-Correlated Data with Varying Inter-Class Penalties"
and with
G. Visser,
D. L. Dowe and J. P. Uotila (2009)
"Enhancing MML Clustering using
Context Data with Climate Applications".
Russell Edwards and David Dowe
created a version of Snob (also in 1998) which deals with
single Gaussian factor analysis in sequentially and spatially uncorrelated
data. It uses total assignment. (See
publications.)
Yudi Agusta and
David Dowe
have also developed
MML
mixture modelling
software for
other correlated
Gaussians,
t distributions
(2002) and
Gamma distributions
(2003,
.pdf);
and Jon Oliver and David Dowe published a
note (1996) on
MML
mixture modelling
of von Mises-Fisher spherical distributions.
A useful set of links on Snob is given in the next few lines immediately below:
Snob ReadMe,
documentation
and
(data) sd1.raw
files,
.ps of Wallace and Dowe (1997)
and
.pdf
of
more recent, 2000, paper:
Wallace, C.S.
and
D. L. Dowe
(2000).
MML clustering of multi-state, Poisson, von Mises circular
and Gaussian distributions,
Statistics and Computing, Vol. 10, No. 1, Jan. 2000,
pp73-83.
p73,
p74,
p75,
p76,
p77,
p78,
p79,
p80,
p81,
p82,
p83
http://www.wkap.nl/issuetoc.htm/0960-3174+10+1+2000
http://www.wkap.nl/sampletoc.htm?0960-3174+10+1+2000
See also
Wallace and Dowe (1994b)
Wallace, C.S. and
D.L. Dowe (1994b),
Intrinsic classification by MML - the Snob program.
Proc.
7th
Australian Joint Conf. on Artificial Intelligence,
UNE, Armidale, Australia, November 1994,
pp37-44.
http://www.csse.monash.edu.au/~dld/Publications/1994/Wallace+Dowe1994.ref
http://www.csse.monash.edu.au/~dld/Publications/1994/Wallace+Dowe1994_IntrinsicClassification_MML_AI94.Snob.pdf
http://www.csse.monash.edu.au/~dld/Publications/1994/Wallace+Dowe1994_IntrinsicClassification_MML_AI94.Snob.ps
(reference
here)
in
C.S. Wallace
publications and/or
D.L. Dowe publications.
The
Snob software
is available
subject to conditions.
Snob Method: Bayesian,
Minimum Message Length
(MML).
Features: Deals with missing data.
Papers on theory behind Snob
and
papers on applications of Snob.
Chris
Wallace MML publications, 1968-1991 and
Chris
Wallace MML publications, 1990- .
Chris
Wallace MML applications, 1968-1996
and
Chris
Wallace MML applications, 1990- .
Fortran compiler
If you would like to download a
Linux
Fortran compiler,
go to
http://www.rpmfind.org/RPM/EByName.html
and look for "egcs-g77...".
C, C++, Java version(s)
A C version is currently under construction.
Link to Random number generation software
(Pseudo-)Random
number generation software in Fortran :
uniform (for multinomial),
Gaussian
(Normal),
von Mises
(circular) and
Poisson.
http://www.csse.monash.edu.au/research/mdmc/software/random.
Other links
Link to Lloyd Allison's Short note on Snob.
Link to K D Mine's S*i*ftware Snob notes, based on material supplied by D. Dowe and L. Allison.
Minimum Description Length
(MDL) and
comparisons
with MML
(on
pp270-283
and elsewhere)
in
Comp.
J., Vol 42, No. 4, 1999.
Bayesian networks using
MML,
clustering, mixture modelling and
finite mixture models,
comparisons between
MML and the subsequent
Minimum Description Length principle,
data repositories,
decision trees and
decision graphs
using MML,
medical research,
"Minimum Message Length, MDL and Generalised Bayesian Networks with Asymmetric
Languages",
by J. W. Comley and D.L. Dowe;
Chapter 11
(pp265-294)
in P. Grunwald, M. A. Pitt and I. J. Myung (eds.),
Advances in Minimum Description Length:
Theory and Applications,
M.I.T. Press, April 2005, ISBN 0-262-07262-9.
{This is about Generalised Bayesian nets, generalising MML Bayesian nets or
MML Bayesian networks or MML Bayes nets (or Generalised directed graphical
models, generalising MML directed graphical models); and it deals with
a mix of both continuous and discrete variables.
(See also
Comley and Dowe
(2003),
.pdf.)}
Minimum Message Length
(MML),
Occam's razor
(Ockham's razor),
a probabilistic sports prediction
competition
(and further reading on probabilistic
scoring),
Snob
theory publications and
applications publications,
(econometric)
time series
using MML,
Chris Wallace (1933-2004)
(developer of MML in
1968)
and
his (2005) [posthumous]
Book:
Statistical and Inductive Inference by Minimum Message
Length, Springer (Series: Information Science and Statistics), 2005, XVI,
432 pp., 22 illus., Hardcover, ISBN: 0-387-23795-X.
(Link to
table of
contents,
chapter headings and
more.)
Tribute to Chris Wallace:
D. L. Dowe (2008),
"Foreword
re C. S. Wallace",
Computer Journal,
Vol. 51, No. 5
(Sept. 2008)
[Christopher
Stewart WALLACE (1933-2004) memorial special issue],
pp523-560
(and
here).
chess and game theory research,
Feeding the world
(TheHungerSite),
TheRainforestSite,
and
"do-goody"/"do-goody stuff, improving the world and saving the planet".
This Snob page was put together by
Dr David Dowe,
Dept. of Computer Science, Monash University, Clayton, Vic. 3168, Austra
lia
e-mail:
d l d at
csse dot monash.edu.au
(Fax: +61 3 9905-5146)
(and was started on Sat 8th Mar. 1997) and was last updated no earlier
than Mon 3rd Mar. 1998.
Copyright
David L. Dowe,
Monash University, Australia,
8 Mar 1997, 3 Mar 1998, 7 May 1998, etc.
Copying is not permitted without expressed permission from
David L. Dowe.