Subject: FINAL Call for Papers IDA97
From: Michael Berthold
Date: Tue, 07 Jan 1997 16:31:20 +0100
=========>>> DEADLINE FOR SUBMISSIONS: FEBRUARY 1st, 1997 <<<===========
FINAL CALL FOR PAPERS
The Second International Symposium on Intelligent Data Analysis (IDA-97)
Birkbeck College, University of London
4th-6th August 1997
In Cooperation with
AAAI, ACM SIGART, BCS SGES, IEEE SMC, and SSAISB
[ http://web.dcs.bbk.ac.uk/ida97.html ]
Objective
=========
For many years the intersection of computing and data analysis contained
menu-based statistics packages and not much else. Recently, statisticians
have embraced computing, computer scientists are using statistical theories
and methods, and researchers in all corners are inventing algorithms to find
structure in vast online datasets. Data analysts now have access to tools
for exploratory data analysis, decision tree induction, causal induction,
function finding, constructing customised reference distributions, and
visualisation. There are prototype intelligent assistants to advise on
matters of design and analysis. There are tools for traditional, relatively
small samples and for enormous datasets.
The focus of IDA-97 will be "Reasoning About Data". We are interested in
intelligent systems that reason about how to analyze data, perhaps as human
analysts do. Analysts often bring exogenous knowledge about data to bear
when they decide how to analyze it; they use intermediate results to decide
how to proceed; they reason about how much analysis the data will actually
support; they consider which methods will be most informative; they decide
which aspects of a model are most uncertain and focus attention there; they
sometimes have the luxury of collecting more data, and plan to do so
efficiently. In short, there is a strategic aspect to data analysis, beyond
the tactical choice of this or that test, visualisation or variable.
Topics
======
The following topics are of particular interest to IDA-97:
* APPLICATIONS & TOOLS
- analysis of different kinds of data (e.g., censored, temporal etc)
- applications (e.g., commerce, engineering, finance, legal,
manufacturing, medicine, public policy, science)
- assistants, intelligent agents for data analysis
- evaluation of IDA systems
- human-computer interaction in IDA
- IDA systems and tools
- information extraction, information retrieval
* THEORY & GENERAL PRINCIPLES
- analysis of IDA algorithms
- bias
- classification
- clustering
- data cleaning
- data pre-processing
- experiment design
- model specification, selection, estimation
- reasoning under uncertainty
- search
- statistical strategy
- uncertainty and noise in data
* ALGORITHMS & TECHNIQUES
- Bayesian inference and influence diagrams
- bootstrap and randomization
- causal modeling
- data mining
- decision analysis
- exploratory data analysis
- fuzzy, neural and evolutionary approaches
- knowledge-based analysis
- machine learning
- statistical pattern recognition
- visualization
Submissions
===========
Participants who wish to present a paper are requested to submit a manu-
script, not exceeding 10 single-spaced pages. We strongly encourage that
the manuscript is formatted following the Springer's "Advice to Authors
for the Preparation of Contributions to LNCS Proceedings" which can be
found on the IDA-97 web page. This submission format is identical to the
one for the final camera-ready copy of accepted papers. In addition, we
request a separate page detailing the paper title, authors' names, postal
and email addresses, phone and fax numbers.
Email submissions in Postscript form are encouraged. Otherwise, five hard
copies of the manuscripts should be submitted.
Submissions should be sent to the IDA-97 Program Chairs:
Central, North and South America: Elsewhere:
Paul Cohen Xiaohui Liu
Department of Computer Science Department of Computer Science
Lederle Graduate Research Center Birkbeck College
University of Massachusetts, Amherst University of London
Amherst, MA 01003-4610 Malet Street
USA London WC1E 7HX, UK
cohen@cs.umass.edu hui@dcs.bbk.ac.uk
IMPORTANT DATES
February 1st, 1997 Submission of papers
April 15th, 1997 Notification of acceptance
May 15th, 1997 Final camera ready paper
Review
======
All submissions will be reviewed on the basis of relevance, originality,
significance, soundness and clarity. At least two referees will review
each submission independently. Results of the review will be send to the
first author via email, unless requested otherwise.
Publications
============
Papers which are accepted and presented at the conference will appear in
the IDA-97 proceedings, to be published by Springer-Verlag in its Lecture
Notes in Computer Science series. Authors of the best papers will be
invited to extend their papers for further review for a special issue of
"Intelligent Data Analysis: An International Journal".
IDA-97 Organisation
===================
General Chair: Xiaohui Liu
Program Chairs: Paul Cohen, Xiaohui Liu
Steering Comm. Chair: Paul Cohen, University of Massachusetts, USA
Exhibition Chair: Richard Weber, MIT GmbH, Aachen, Germany
Finance Chair: Sylvie Jami, Birkbeck College, UK
Local Arrangements Chair: Trevor Fenner, Birkbeck College, UK
Public. and Proc. Chair: Michael Berthold, University of Karlsruhe, Germany
Sponsorship Chair: Mihaela Ulieru, Simon Fraser University, Canada
Steering Committee
Michael Berthold University of Karlsruhe, Germany
Fazel Famili National Research Council, Canada
Doug Fisher Vanderbilt University, USA
Alex Gammerman Royal Holloway London, UK
David Hand Open University, UK
Wenling Hsu AT&T; Consumer Lab, USA
Xiaohui Liu Birkbeck College, UK
Daryl Pregibon AT&T; Research, USA
Evangelos Simoudis IBM Almaden Research, USA
Program Committee
Eric Backer Delft University of Technology, The Netherlands
Riccardo Bellazzi University of Pavia, Italy
Michael Berthold University of Karlsruhe, Germany
Carla Brodley Purdue University, USA
Gongxian Cheng Birkbeck College, UK
Fazel Famili National Research Council, Canada
Julian Faraway University of Michigan, USA
Thomas Feuring WWU Muenster, Germany
Alex Gammerman Royal Holloway London, UK
David Hand The Open University, UK
Rainer Holve Forwiss Erlangen, Germany
Wenling Hsu AT&T; Research, USA
Larry Hunter National Library of Medicine, USA
David Jensen University of Massachusetts, USA
Frank Klawonn University of Braunschweig, Germany
David Lubinsky University of Witwatersrand, South Africa
Ramon Lopez de Mantaras Artificial Intelligence Research Institute, Spain
Sylvia Miksch Vienna University of Technology, Austria
Rob Milne Intelligent Applications Ltd, UK
Gholamreza Nakhaeizadeh Daimler-Benz Forschung und Technik, Germany
Claire Nedellec Universite Paris-Sud, France
Erkki Oja Helsinki University of Technology, Finland
Henri Prade University Paul Sabatier, France
Daryl Pregibon AT&T; Research, USA
Peter Ross University of Edinburgh, UK
Steven Roth Carnegie Mellon University, USA
Lorenza Saitta University of Torino, Italy
Peter Selfridge AT&T; Research, USA
Rosaria Silipo University of Florence, Italy
Evangelos Simoudis IBM Almaden Research, USA
Derek Sleeman University of Aberdeen, UK
Paul Snow Delphi, USA
Rob St. Amant North Carolina State University, USA
Lionel Tarassenko Oxford University, UK
John Taylor King's College London, UK
Loren Terveen AT&T; Research, USA
Hans-Juergen Zimmermann RWTH Aachen, Germany
Enquiries
=========
Detailed information regarding IDA-97 can be found on the World Wide Web
Server of the Department of Computer Science at Birkbeck College, London:
http://web.dcs.bbk.ac.uk/ida97.html
Apart from presentation of research papers, IDA-97 also welcomes demonstr-
ations of software and publications related to intelligent data analysis
and welcomes those organisations who may wish to partly sponsor the confe-
rence.
Relevant enquiries may be sent to appropriate chairs whose details can be
found in the above-mentioned IDA-97 web page, or to
IDA-97 Administrator
Department of Computer Science
Birkbeck College
Malet Street
London WC1E 7HX, UK
E-mail: ida97-enquiry@dcs.bbk.ac.uk
Tel: (+44) 171 631 6722
Fax: (+44) 171 631 6727
There is also a moderated IDA-97 discussion list. To subscribe, send the
word "subscribe" in the message body to:
ida97-request@dcs.bbk.ac.uk
Subject: Probability and Wheels: Connections and Closing the Gap
From: bm373592@muenchen.org (Uenal Mutlu)
Date: Tue, 07 Jan 1997 15:25:42 GMT
PROBABILITIES AND WHEELS, COVERING DESIGNS, LOTTO etc.
In previous discussions all probability calculations which were still
open were finally solved I think (correct me if still something is
outstanding).
Now, I made the following IMHO interessting observation: we've seen (cf.
table below) that the probability for 6/49 type game using only 1 ticket
for AT LEAST 3 matching numbers is 1.86375% (= 1 in 53.6551) and
for EXACT 3 matching numbers is 1.76504% (= 1 in 56.6559)
Ie. either playing the _same_ 1 ticket in 54 drawings, or equally simply
playing 54 _randomly_ selected _different_ tickets in 1 drawing, should
give AT LEAST once 3 or more correct numbers in both cases.
Now, the connection to the wheels and covering designs: There exists
a wheel which assures _always_ at least once 3 correct numbers; it is
built up of 168 single tickets (IMHO it's the shortest known today
for 6/49 which always guarantees a win (>= 3)).
So, the interessting question is: why should one ever play the
168 ticket wheel and not simply 54 randomly choosen different single
tickets? IMHO mathematically spoken both cases should offer nearly
the same assurance and probability for hitting once or more at least
3 correct numbers. (this maybe not 100% correct, but you can imagine
what I mean).
A further question arises: why does such a wheel have so many tickets,
whereas the probability calculations show us that on average only
54 are needed for AT LEAST 3. (Ok, I also would accept 100 tickets
or so, but why even more than 3 times 54 ?!)
I think this deserves further investigation and research, not only for
the player but also for the wheel designers and researchers.
Personally, I would recommend the interessted player to go with the
54 tickets, and recommend the wheel designers and researchers in
Design Theory (Covering Designs etc.) to also take into consideration
such probability calculations. Any comments?
Here again the whole table ignoring the bonus number (see r.g.l. for
tables with the bonus nbr, or email me)
vAll=49 vSub=6 k=6: Cn(49,6)=13983816 Cn(6,6)=1
m p cumul(p) 1:p 1:cumul(p)
(=match) (=EXACT) (=AT LEAST) (=EXACT) (=AT LEAST)
------------------------------------------------------------------------
6 0.0000000715112 0.0000000715112 13983816.00000 13983816.00000
5 0.0000184498995 0.0000185214108 54200.83721 53991.56757
4 0.0009686197244 0.0009871411352 1032.39690 1013.02637
3 0.0176504038669 0.0186375450020 56.65593 53.65514
2 0.1323780290015 0.1510155740035 7.55412 6.62183
1 0.4130194504848 0.5640350244883 2.42119 1.77294
0 0.4359649755117 1.0000000000000 2.29376 1.00000
Uenal Mutlu
-- Uenal Mutlu (bm373592@muenchen.org)
Math Research, Designs/Codes, Data Compression Algorithms, C/C++
Loc: Istanbul/Turkey + Munich/Germany
Subject: Validity of a test for the IQV (Simpson's index of diversity)
From: mglacy@lamar.ColoState.EDU (Michael Lacy)
Date: 7 Jan 1997 08:32:51 -0700
My question concerns what I see as a logical flaw in a hypothesis test proposed
for the Index of Qualitative Variation (aka Simpson's Index of Diversity), which
is a measure of dispersion for qualitative variables.
[ D = 1 - Sum(p_i^2,i=1..k) ], where p_i is the relative frequency for the ith
of the k categories.
Asymptotic normality and a variance estimator are easily derived by the delta
method. Therefore, a conventional test of H0: D_0 = c has the form z =
(D-c)/Sigma_d, where Sigma_d is a function of the multinomial probability vector.
In practice, the observed sample vector of p_i is used to compute the variance.
At first glance, I thought there was nothing wrong with this variance estimator:
Although the null hypothesis doesn't specify any particular multinomial
probability vector, it seems reasonable to use the available sample data. Or,one
could see this as simply another case of conditioning a test on some feature of
the sample data.
But this sort of conditioning seems wrong, since the particular vector of sample
p_i will, in general, contradict H0. That is, the D value computed on the sample
data will not equal D_0, yet this data is used to compute the variance under the
H0. To me, a more defensible procedure would be to compute the variance using a
probability vector that is consistent with H0. But this is problematic, since for
any hypothesized value of D_0, many different vectors of p_i are consistent with
it. And, the value of the variance in general differs substantially depending on
which one of these is used to compute the variance.
Thus, I think the conventional test is invalid on its face. This would be
unsurprising, except that quite reputable sources have recommended this test.
I'm about to write a piece suggesting (among other things) that this test, and
similar ones for similar measures, are invalid. I've been looking for directly
relevant literature, but haven't found any.
So, my question is: Does my criticism seem sound, or am I missing something?
Thanks,
--
=-=-=-=-=-=-=-=-=-==-=-=-=
Mike Lacy, Sociology Dept., Colo. State Univ. FT COLLINS CO 80523
voice (970) 491-6721 fax (970) 491-2191
Subject: Re: Probability and Wheels: Connections and Closing the Gap
From: Karl Schultz
Date: Tue, 07 Jan 1997 13:23:44 -0700
Uenal Mutlu wrote:
>
> PROBABILITIES AND WHEELS, COVERING DESIGNS, LOTTO etc.
>
> In previous discussions all probability calculations which were still
> open were finally solved I think (correct me if still something is
> outstanding).
>
> Now, I made the following IMHO interessting observation: we've seen (cf.
> table below) that the probability for 6/49 type game using only 1 ticket
> for AT LEAST 3 matching numbers is 1.86375% (= 1 in 53.6551) and
> for EXACT 3 matching numbers is 1.76504% (= 1 in 56.6559)
>
> Ie. either playing the _same_ 1 ticket in 54 drawings, or equally simply
> playing 54 _randomly_ selected _different_ tickets in 1 drawing, should
> give AT LEAST once 3 or more correct numbers in both cases.
It is likely, but not guarenteed. You would EXPECT to have a 3-win
after playing 54 tickets, but with such a small sample, you would
still have a good chance of wildly fluctuating results.
> Now, the connection to the wheels and covering designs: There exists
> a wheel which assures _always_ at least once 3 correct numbers; it is
> built up of 168 single tickets (IMHO it's the shortest known today
> for 6/49 which always guarantees a win (>= 3)).
>
> So, the interessting question is: why should one ever play the
> 168 ticket wheel and not simply 54 randomly choosen different single
> tickets? IMHO mathematically spoken both cases should offer nearly
> the same assurance and probability for hitting once or more at least
> 3 correct numbers. (this maybe not 100% correct, but you can imagine
> what I mean).
Because the 168-wheel is a 100% guarentee. If you absolutely,
positively want to ensure at least a 3-win, the best way to do it is
to use this wheel.
I see what you are getting at, saying that 54 tickets would give you
a darned good chance, but the price to pay for the 100% certain 3-win
is the larger wheel.
> A further question arises: why does such a wheel have so many tickets,
> whereas the probability calculations show us that on average only
> 54 are needed for AT LEAST 3. (Ok, I also would accept 100 tickets
> or so, but why even more than 3 times 54 ?!)
Again, the price to pay for ensuring the 3-win.
> I think this deserves further investigation and research, not only for
> the player but also for the wheel designers and researchers.
Yes, a good question. Since it is more statistical, maybe Sharkey can
help compute it.
The question being, just how likely is it to win a 3-match while
playing 54 draws.
> Personally, I would recommend the interessted player to go with the
> 54 tickets, and recommend the wheel designers and researchers in
> Design Theory (Covering Designs etc.) to also take into consideration
> such probability calculations. Any comments?
Any approach where you play more tickets helps, if you really must
play at all.
> Here again the whole table ignoring the bonus number (see r.g.l. for
> tables with the bonus nbr, or email me)
>
> vAll=49 vSub=6 k=6: Cn(49,6)=13983816 Cn(6,6)=1
> m p cumul(p) 1:p 1:cumul(p)
> (=match) (=EXACT) (=AT LEAST) (=EXACT) (=AT LEAST)
> ------------------------------------------------------------------------
> 6 0.0000000715112 0.0000000715112 13983816.00000 13983816.00000
> 5 0.0000184498995 0.0000185214108 54200.83721 53991.56757
> 4 0.0009686197244 0.0009871411352 1032.39690 1013.02637
> 3 0.0176504038669 0.0186375450020 56.65593 53.65514
> 2 0.1323780290015 0.1510155740035 7.55412 6.62183
> 1 0.4130194504848 0.5640350244883 2.42119 1.77294
> 0 0.4359649755117 1.0000000000000 2.29376 1.00000
Interested people can check these against the odds usually printed on
play slips, since the 6-subset is the game that is really played.
>
> Uenal Mutlu
>
> -- Uenal Mutlu (bm373592@muenchen.org)
> Math Research, Designs/Codes, Data Compression Algorithms, C/C++
> Loc: Istanbul/Turkey + Munich/Germany
Subject: Re: Qu: pdf of a/b ??
From: startz@u.washington.edu (Dick Startz)
Date: Tue, 07 Jan 1997 12:20:38 -0800
Three definitive references on the distribution of the ratio of normal
random variables are:
Fieller, E.C. (1932), łThe distribution of the index in a normal bivariate
population,˛ Biometrika 24, 428-40.
Geary, R. C. (1930), The Frequency Distribution of the Quotient of Two
Normal Variates,˛ J. of the Royal Statistical Society, Series A, 93,
442-446.
Hinckley, D. V. (1969), łOn the Ratio of Two Correlated Normal Random
Variables,˛ Biometrika, 56, 635-639.
I would try the last one first.
-Dick Startz
In article , c_gordon@igkw2.agric.za wrote:
> Hi,
>
> Is it possible to analytically express the probability distribution for
>
> c = a / b
>
> where a and b are univariate independent normally distributed random
variables.
>
snip
>
> Regards,
> ---
> Christopher Gordon Tel. (012) 326-4205 (w)
> Remote Sensing Fax. (012) 323-1157
> Inst. for Soil, Climate and Water email: c_gordon@igkw2.agric.za
> Pretoria, South Africa chris@bayes.agric.za
> Standard disclaimers apply.
--
Richard Startz Internet:: startz@u.washington.edu
Professor of Economics voice:: 206-543-8172
University of Washington fax:: 206-685-7477
Seattle, WA 98195-3330 USA
Subject: Computational Finance at the Oregon Graduate Institute
From: Computational Finance
Date: Tue, 7 Jan 1997 17:45:29 -0800
=======================================================================
COMPUTATIONAL FINANCE at the Oregon Graduate Institute of
Science & Technology (OGI)
Master of Science Concentrations in
Computer Science & Engineering (CSE)
Electrical Engineering (EE)
Now Reviewing MS Applications for Fall 1997!
New! Certificate Program Designed for Part-Time Students.
For more information, contact OGI Admissions at (503)690-1027 or
admissions@admin.ogi.edu, or visit our Web site at:
http://www.cse.ogi.edu/CompFin/
=======================================================================
Computational Finance Overview:
Advances in computing technology now enable the widespread use of
sophisticated, computationally intensive analysis techniques applied
to finance and financial markets. The real-time analysis of
tick-by-tick financial market data, and the real-time management
of portfolios of thousands of securities is now sweeping the
financial industry. This has opened up new job opportunities for
scientists, engineers, and computer science professionals in the
field of Computational Finance.
The strong demand within the financial industry for technically-
sophisticated graduates is addressed at OGI by the Master of
Science and Certificate Programs in Computational Finance. Unlike
a standard two year MBA, the programs are directed at training
scientists, engineers, and technically oriented financial professionals
in the area of quantitative finance.
The master's programs lead to a Master of Science in Computer Science
and Engineering (CSE track) or in Electrical Engineering (EE track).
The MS programs can be completed within 12 months on a full-time
basis. In addition, OGI has introduced a Certificate program
designed to provide professionals in engineering and finance a means
of upgrading their skills or acquiring new skills in quantitative
finance on a part-time basis.
The Computational Finance MS concentrations feature a unique
combination of courses that provides a solid foundation in finance
at a non-trivial, quantitative level, plus the essential
core knowledge and skill sets of computer science or the information
technology areas of electrical engineering. These skills are
important for advanced analysis of markets and for the development
of state-of-the-art investment analysis, portfolio management,
trading, derivatives pricing, and risk management systems.
The MS in CSE is ideal preparation for students interested in
securing positions in information systems in the financial industry,
while the MS in EE provides rigorous training for students interested
in pursuing careers as quantitative analysts at leading-edge
financial firms.
The curriculum is strongly project-oriented, using state-of-the-art
computing facilities and live/historical data from the world's
major financial markets provided by Dow Jones Telerate. Students
are trained in the use of high-level numerical and analytical
software packages for analyzing financial data.
OGI has established itself as a leading institution in research
and education in Computational Finance. Moreover, OGI has strong
research programs in a number of areas that are highly relevant
for work in quantitative analysis and information systems in the
financial industry.
-----------------------------------------------------------------------
Admissions
-----------------------------------------------------------------------
Applications for entrance into the Computational Finance MS programs
for Fall Quarter 1997 are currently being considered. The deadlines
for receipt of applications are:
January 15 (Early Decision Deadline, decisions by February 15)
March 15 (Final Deadline, decisions by April 15)
A candidate must hold a bachelor's degree in computer science,
engineering, mathematics, statistics, one of the biological or
physical sciences, finance, econometrics, or one of the quantitative
social sciences. Candidates who hold advanced degrees in these
fields or who have experience in the financial industry are also
encouraged to apply.
Applications for the Certificate Program are considered on an
ongoing basis for entrance in any quarter.
----------------------------------------------------------------------
Contact Information
----------------------------------------------------------------------
For general information and admissions materials:
Visit our web site at:
http://www.cse.ogi.edu/CompFin/
or contact:
Office of Admissions
Oregon Graduate Institute
P.O.Box 91000
Portland, OR 97291-1000
E-mail: admissions@admin.ogi.edu
Phone: (503)690-1027
For special inquiries:
E-mail: compfin@cse.ogi.edu
======================================================================
