Maximum Entropy and Bayesian Methods: Boise, Idaho, USA, 1997 Proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis

299 Pages·1998·15.709 MB·English

by  John Skilling (auth.), Gary J. Erickson, Joshua T. Rychert, C. Ray Smith (eds.)

Maximum Entropy and Bayesian Methods
Fundamental Theories of Physics
An International Book Series on The Fundamental Theories ofP hysics:
Their Clarification, Development and Application
Editor:
ALWYN VAN DER MERWE, University ofD enver, U.S.A.
Editoral Advisory Board:
LAWRENCE P. HORWITZ, Tel-Aviv University, Israel
BRIAN D. JOSEPHSON, University of Cambridge, U.K.
CLIVE KILMISTER, University ofL ondon, U.K.
PEKKA J. LAHTI, University ofTurku, Finland
GUNTER LUDWIG, Philipps-Universitiit, Marburg, Germany
NATHAN ROSEN, Israel Institute of Technology, Israel
ASHER PERES, Israel Institute of Technology, Israel
EDUARD PRUGOVECKI, University of Toronto, Canada
MENDEL SACHS, State University ofN ew York at Buffalo, U.S.A.
ABDUS SALAM, International Centre for Theoretical Physics, Trieste, Italy
HANS-JURGEN TREDER, Zentralinstitut/iir Astrophysik der Akademie der Wissenschaften,
Germany
Volume 98
Maximum Entropy and
Bayesian Methods
Boise, Idaho, D.S.A., 1997
Proceedings of the i7th international Workshop on Maximum
Entropy and Bayesian Methods of Statistical Analysis
edited by
Gary J. Erickson
Joshua T. Rychert
Department ofE lectronical Engineering,
Boise State University,
Boise, ldaho, U.S.A.
and
C. Ray Smith
Fayetteville, Tennessee, U.S.A.
SPRINGER-SCIENCE+BUSINESS MEDIA, B.V.
A C.I.P. Catalogue record for this book is available from the Library of Congress.
ISBN 978-94-010-6111-7 ISBN 978-94-011-5028-6 (eBook)
DOI 10.1007/978-94-011-5028-6
Printed on acid-free pap er
AlI Rights Reserved
© 1998 Springer Science+Business Media Dordrecht
Originally published by Kluwer Academic Publishers in 1998
Softcover reprint ofthe hardcover Ist edition 1998
No part ofthe material protected by this copyright notice may be reproduced or
utilized in any form or by any means, electronic or mechanical,
including photocopying, recording or by any information storage and
retrieval system, without written permission from the copyright owner
CONTENTS
IN MEMORY OF EDWIN T. JAYNES ................................... vii
PREFACE ....................................................................................................................... .ix
MASSIVE INFERENCE AND MAXIMUM ENTROPY
John Skilling. ...................................................................................................... 1
CV -NP BA YESIANISM BY MCMC
Carlos Rodriguez ............................................................................................. 15
WHICH ALGORITHMS ARE FEASIBLE? A MAXENT APPROACH
D. E. Cooke, V. Kreinovich, and L. Longpre ................................................... 25
MAXIMUM ENTROPY, LIKELIHOOD AND UNCERTAINTY: A COMPARISON
Amos Golan ..................................................................................................... .3 5
PROBABILISTIC METHODS FOR DATA FUSION
Ali Mohammed-Djafari ..................................................................................... 57
WHENCE THE LAWS OF PROBABILITY?
Anthony J. M. Garrett ....................................................................................... 71
BAYESIAN GROUP ANALYSIS
W. von der Linden, V. Dose, and A. Ramaswami ............................................ 87
SYMMETRY-GROUP JUSTIFICATION OF MAXIMUM ENTROPY METHOD
AND GENERALIZED MAXIMUM ENTROPY METHODS IN IMAGE
PROCESSING
Olga Kosheleva ............................................................................................... 101
PROBABILITY SYNTHESIS, HOW TO EXPRESS PROBABILITIES IN TERMS OF
EACH OTHER
Anthony 1. M. Garrett ..................................................................................... 115
INVERSION BASED ON COMPUTATIONAL SIMULATIONS
Kenneth Hanson, G. S. Cunningham, and S. S. Saquih ................................ .l21
MODEL COMPARISON WITH ENERGY CONFINEMENT DATA FROM LARGE
FUSION EXPERIMENTS
R. Preuss, V. Dose, and W. von der Linden ................................................... 137
DECONVOLUTION BASED ON EXPERIMENTALLY DETERMINED
APPARATUS FUNCTIONS
V. Dose, R. Fischer, and W. von der Linden .................................................. 147
A BAYESIAN APPROACH FOR THE DETERMINATION OF THE CHARGE
DENSITY FROM ELASTIC ELECTRON SCATTERING DATA
A. Mohammad-Djafari and H. G. Miller ........................................................ 153
vi CONTENTS
INTEGRAT ED DEFORMABLE BOUNDARY FINDING USING BAYESIAN
STRATEGIES
Amit Chakraborty and James Duncan ............................................................ 171
SHAPE RECONSTRUCTION IN X-RAY TOMOGRAPHY FROM A SMALL
NUMBER OF PROJECTIONS USING DEFORMABLE MODELS
Ali Mohammad-Djafari and Ken Sauer. ......................................................... 183
AN EMPIRICAL MODEL OF BRAIN SHAPE
James Gee and L. Le Briquer. ......................................................................... 199
DIFFICULTIES APPLYING BLIND SOURCE SEPARATION TECHNIQUES TO
EEGANDMEG
Kevin H. Knuth ............................................................................................... 209
THE HISTORY OF PROBABILITY THEORY
Anthony J. M. Garrett ..................................................................................... 223
WE MUST CHOOSE THE SIMPLEST PHYSICAL THEORY: LEVIN-LI-VITANYI
THEOREM AND ITS POTENTIAL PHYSICAL APPLICATIONS
D. Fox, M. Schmidt, M. Koshelev,
V. Kreinovich, L. Longpre, and J. Kuhn ......................................................... 239
MAXIMUM ENTROPY AND ACAUSAL PROCESSES: ASTROPHYSICAL
APPLICATIONS AND CHALLENGES
M. Koshe1ev ................................................................................................... .253
COMPUTATIONAL EXPLORATION OF THE ENTROPIC PRIOR OVEk SPACES
OF LOW DIMENSIONALITY
Holly E. Fitzgerald and Everett G. Larson ..................................................... .263
ENVIRONMENTA LLY -ORIENTED PROCESSING OF MULTI-SPECTRAL
SATELLITE IMAGES: NEW CHALLENGES FOR BAYESIAN METHODS
S. A. Starks and V. Kreinovich ...................................................................... .271
MAXIMUM ENTROPY APPROACH TO OPTIMAL SENSOR PLACEMENT FOR
AEROSPACE NON-DESTRUCTIVE TESTING
R. Osegueda, C. Ferregut, M.J. George,
J. M. Gutierrez, and V. Kreinovich ............................................................... .277
MAXIMUM ENTROPY UNDER UNCERTAINTY
Henryk Gzyl. ................................................................................................... 291
SUBJECT INDEX ......................................................................................................... 296
IN MEMORY OF EDWIN T. JAYNES
With the passing of Edwin Thompson Jaynes on April 30, 1998,
his many friends in the MAXENT community and beyond must say
good-bye to a very special person.
His openness and unselfishness, his independent and original
thought, and his uncompromising high standards have made an indelible
impact. His written work was so lucid that it was in and of itself a
pleasure to read; his speaking style was every bit as penetrating and
intelligible as his writing.
Beyond his prodigious scientific contributions and wisdom, much
more could be said about those personal qualities which made Ed's
friendship over the years a rare privilege. But as anyone who knew him
understands, Ed believed that such matters are by their nature private, and
would have been uncomfortable with public profession of the grief which
naturally accompanies this loss.
He will be keenly missed.
vii
PREFACE
This volume has its origin in the Seventeenth International Workshop
on Maximum Entropy and Bayesian Methods, MAXENT 97. The
workshop was held at Boise State University in Boise, Idaho, on August
4 - 8, 1997. As in the past, the purpose of the workshop was to bring
together researchers in different fields to present papers on applications of
Bayesian methods (these include maximum entropy) in science,
engineering, medicine, economics, and many other disciplines.
Thanks to significant theoretical advances and the personal
computer, much progress has been made since our first Workshop in
1981. As indicated by several papers in these proceedings, the subject has
matured to a stage in which computational algorithms are the objects of
interest, the thrust being on feasibility, efficiency and innovation. Though
applications are proliferating at a staggering rate, some in areas that
hardly existed a decade ago, it is pleasing that due attention is still being
paid to foundations of the subject. The following list of descriptors,
applicable to papers in this volume, gives a sense of its contents:
deconvolution, inverse problems, instrument (point-spread) function,
model comparison, multi sensor data fusion, image processing,
tomography, reconstruction, deformable models, pattern recognition,
classification and group analysis, segmentation/edge detection, brain
shape, marginalization, algorithms, complexity, Ockham's razor as an
inference tool, foundations of probability theory, symmetry, history of
probability theory and computability.
MAXENT 97 and these proceedings could not have been brought to
final form without the support and help of a number of people. In
particular, SCP Global Technologies helped with the realization of
MAXENT 97 . The editors, Gary Erickson, Josh Rychert, and Ray Smith,
express their gratitude to all the speakers and are appreciative for the
additional time and effort authors expended in producing a finished
manuscript.
This preface must end on a sad note: Professor E. T. Jaynes died on
April 30, 1998, in St. Louis, Missouri.
ix
MASSIVE INFERENCE AND MAXIMUM ENTROPY
JOHN SKILLING
Department of Applied Mathematics and Theoretical Physics
University of Cambridge
England CB3 9EW
Abstract. In data analysis, maximum entropy (MaxEnt) has been used to re
construct measures i.e. positive, additive distributions) from limited data. The
MaxEnt prior was originally derived from the "monkey model" in which quanta
of uniform intensity could appear randomly in the field of view. To avoid undue
digitisation, the quanta had to be small, and this led to difficulties with the Law
of Large Numbers, and to unavoidable approximations in computing the poste
rior. A better way of avoiding digitisation is to give the quanta variable intensity
with an exponential prior, that being the natural MaxEnt assignment. We call
this technique "Massive Inference" (MassInf). Although the entropy formula no
longer appears in the prior, MassInf results show improved quality. MassInf is also
capable of assigning a simple prior for polarized images.
Key words: Maximum entropy, infinitely divisible, polarization, regularization
1. History of maximum entropy
As presented by Jaynes (1957), the Principle of Maximum Entropy (PME) is a
rule for assigning probability distributions: in making inferences on the basis of
partial infomation we are to use that probability distribution which has maximum
entropy subject to whatever ensemble-average constraints are known. Given some
mean values
(1)
that include normalisation L: Pi = 1, the probability distribution p is to be assigned
by maximising its entropy
(2)
(Shannon, 1948) subject to the given constraints. In statistical mechanics, the
entropy S is derivable from the combinatoric number of ways
II
n = N!I nil ~ exp(S), (3)
G. J. Erickson et al. (eds.), Maximum Entropy and Bayesian Methods
© Springer Science+Business Media Dordrecht 1998
2 JOHN SKILLING
of dividing an ensemble of N systems into cells with occupation ni having mean N Pi.
Alternatively, if individual mean values mi are assigned, each occupation number
can be given a Poisson distribution with correct mean, leading to
The PME amounts to the entirely reasonable prescription of giving equal in
trinsic weight to each individual state. When the constraints D include a defining
set of physical variables such as energy and volume, the maximum entropy dis
tribution gives accurate predictions of other average quantities with different R.
Fluctuations are generally O(N-l/2) small, and deviations larger than this indi
cate an unacknowledged extra constraint.
It is tempting and productive to apply this successful formalism to data anal
ysis, and this has been done both directly and indirectly. Indirect applications
use PME to assign the posterior probability distribution Pr(fID) of the quan
tity f being sought. It is supposed that the data are ensemble-average constraints
Dk = J Rk(f) Pr(f)df, so that the PME becomes applicable. The commonest such
application is the derivation of a power spectrum from autocorrelation coeffi
cients D of a time-series f. Actually, data tend to be observations of the particular
object being investigated at the time, and the proper analysis is Bayesian. A prior
probability Pr(f) needs to be assigned (by PME or other insight), but it is then
not determined by the data, whose role is to modulate the prior through the usual
likelihood function Pr(DIf).
For a direct application of MaxEnt, we suppose that we seek the distribution f
of some positive, additive quantity such as the intensity of light in an image, or the
flux of energy along a spectrum. Mathematicians call such an object a "measure" .
We then proceed with the "monkey model" (Gull and Daniell, 1978) in which f
is identified with a number n of quanta of some unknown but presumed small
strength q. With m similarly rescaled, Stirling's approximation for large n yields
the Quantified Maximum Entropy (QME) prior
II
Pr(f) ex: exp(aS)j fil/2, (5)
where m is a set of weights that models any a priori non-equivalence of the cells i,
and a = 1jq is an unknown but apparently large hyper-parameter. When apply
ing this, a cannot in fact be particularly large, lest the prior dominate the like
lihood. Fortunately, the same entropy form can be derived from symmetry argu
ments which avoid combinatoric modelling (Shore and Johnson, 1980). Related
arguments (Skilling, 1989) suggest the QME prior, implicitly assuming that the
desirable entropy maximum should yield a useful selection from the posterior dis
tribution.
Were the data observations Dk = ~ Rkili to be exact, the PME could be used
to assign a corresponding f, just as in statistical mechanics. Even if the data
are noisy and thus subsumed into a likelihood function Pr(DIf), the PME can
still be used to assign a particular f from among those with some acceptable fit
Pr(f) ~ Po to the data. Visually, distributions assigned by maximum entropy are
often of high quality and utility. Entropy is a good regulariser.