HDTenEightyP/NetNews huggingface dataset:
From: se_karne@rcvie.co.at (Georg Karner)
Subject: Re: Compactness in infinite chess ?
Organization: Alcatel Austria - Elin Research Center, Vienna
Date: Wed, 30 Sep 1992 11:13:13 GMT

There is a very simple counterexample: KR against K. The usual
algorithm works on any finite board. On the other hand, it is not
possible to construct a mate position with the same pieces on an
infinite board. (Very clear intuitively, an exact argument is the
following: There are 9 squares to be attacked, but both K and R can
attack only 3 of them.)
-- 
+-- Georg.Karner@rcvie.co.at --- or: se_karne@rcvie.co.at ----------+
|   Georg Karner, Alcatel Austria-ELIN Research Centre,             |
|   A-1210 Vienna, Austria                                          |
+-- fax +43 1 391452 --------------------- voice +43 1 391621 263 --+

Newsgroups: sci.math,rec.games.chess
From: hau4@ellis.uchicago.edu (sven  hauptfeld)
Subject: Re: "Solvability" in Chess
Reply-To: hau4@midway.uchicago.edu
Organization: University of Chicago Computing Organizations
Date: Mon, 28 Sep 1992 23:44:43 GMT

In article <1992Sep28.222143.10073@infodev.cam.ac.uk> cet1@cus.cam.ac.uk (C.E. Thompson) writes:
>
>What relevance does KBN vs. K have here? The deepest variation is well 
>under 50 moves to mate (33, if I remember correctly). There are some
>endgames which do need more than 50 moves without capture or pawn moves
>to execute (most notoriously, KRB vs. KR, but also others such as KBB vs. KN
>and KNN vs. KP) and my understanding was that FIDE rules allowed an extension
>of the 50 moves to 100 in some (but, again notoriously, not all) of these
>situations.
>
I think that the extentions to the 50-move rule were recently revoked. They
began to overcrowd the rules with complexities which apply to a tiny number
of practical games, so the TD's were confused, let alone players who just play
an unrated game, without a TD. So now we are back to the good ol' 50 moves,
and if you reach a position that is theoretically won in 66 moves, too bad.

	Sven

Newsgroups: sci.math
From: holmes@opal.idbsu.edu (Randall Holmes)
Subject: Re: set theory
Organization: Boise State University
Distribution: sci
Date: Tue, 6 Oct 1992 15:12:33 GMT

In article <1992Oct6.021037.6554@math.utexas.edu> epstein@rene.ma.utexas.edu (Paul Epstein) writes:
>Let's not assume the continuum hypothesis.  Let S be a subset of the real
>numbers with cardinality less than that of the reals.
>Does S have Lebesgue measure zero?
>
>(I heard this question through a faculty member at Austin Texas though
>it has probably been asked before).
>
>Paul Epstein

It is possible for a set with cardinality < c to have positive outer
measure--it is then non-measurable.  I don't know the details.



-- 
The opinions expressed		|     --Sincerely,
above are not the "official"	|     M. Randall Holmes
opinions of any person		|     Math. Dept., Boise State Univ.
or institution.			|     holmes@opal.idbsu.edu
Newsgroups: sci.math
From: dak@messua.informatik.rwth-aachen.de (David Kastrup)
Subject: Re: Closed form solution to Sum[1/x,{x,1,n}] ?
Organization: Rechnerbetrieb Informatik  /  RWTH Aachen
Date: 30 Sep 92 23:14:49 GMT

bbm@po.CWRU.Edu (Brian B. Mathewson) writes:


>Is there a closed form formula for:
>  
>  x=n
>  Sum [  1/x  ]    where  x = {1,2,3,...n}  ?
>  x=1
This is the socalled Harmonic number H_n. It is approximately equal to
ln n + gamma + 1/(2n) - 1/(12n^2) + 1/(120n^4) + O(1/n^6),
where the constant in the O term is less than 1 in absolute value.
gamma is Euler's constant = 0.577215664901532860606512090082407

The exact approximation is
ln n + gamma + 1/(2n) - sum(k=1,n) B_{2k}/(2kn^{2k})
+ theta * B_{2m+2}/((2m+2)n^{2m+2}), where 0<theta<1.

Note that this also provides a simple way to calculate gamma to a high
precision (though there are better ways).

See Concrete methematics by Graham/Knuth/Patashnik for more details and
lots of examples of series involving H_n and connections with Zeta functions
and so on.
From: fritz@gems.vcu.edu (J. Fritz)
Newsgroups: sci.math
Subject: Re: ZFC etc. (was Re: Report on Philosophies of Physicists)
Date: 26 Sep 92 14:32:51 -0400
Organization: Virginia Commonwealth University

In article <1992Sep22.232020.14805@CSD-NewsHost.Stanford.EDU>, 
pratt@Sunburn.Stanford.EDU (Vaughan R. Pratt) writes:

[...]
> Without Replacement you can construct a model of Z as the union of the
> set of elements of a sequence whose first element is the set of natural
> numbers (at least I think that's enough) and whose i-th element is the
> powerset of its predecessor.  Call this model M.
[...]
> I will bet $10,000 *against* Z being found inconsistent within the next
> 20 years, with odds of 1000:1 against.  That is, I'm saying that Z
> inconsistent is extremely unlikely, and if you think you can make money
> at any crazy odds on this bet you're the one who's crazy.  If you win
> you get my $10,000, if I win I get only your $10.
[...]

At those odds I can't resist.  In defense of my sanity, let me say that
while I tend to be a Platonist with respect to the integers, I have no
such conviction about the existence of uncountable sets.  (I don't mind
working with them, mind you--I just don't really believe they exist.)

So I think there's a very slight chance that the second step in the
construction of M can't be done.  Furthermore, I seem to recall that Z
and ZC are equiconsistent, just as ZF and ZFC are.  If that's so, then
if the reals can exist, but a well-ordering of them cannot, I'd still
win.

P.S. Just for clarification, I do assume that by Z we mean ZF without
Replacement, but with Separation; that is, for every predicate  p  of
one variable, we have:

      (Aa)(Eb)(Ay)[y in b <--> y in a & p(y)]

       - Fritz    (fritz@ruby.vcu.edu)
                  Disclaimer: nothing I say represents my employer
Newsgroups: sci.math,rec.puzzles
From: aet@mullian.ee.mu.OZ.AU (bert thompson)
Subject: EASY NUMBER SEQUENCE PUZZLE...
Organization: Computer Science, University of Melbourne, Australia
Date: Thu, 1 Oct 1992 13:12:13 GMT

25

1729

635318657

???


what's ??? ?

cheers!

	bert.

--------------------------------------------------------------------------------
bert thompson	aet@munagin.ee.mu.oz.au.
SITEE		school of information technology and electrical engineering
		university of melbourne
--------------------------------------------------------------------------------
From: pcp1@quads.uchicago.edu (patrick carnahan phillips)
Newsgroups: sci.math.stat
Subject: Sequential Bonferroni: OK to use?
Date: 29 Sep 92 14:26:01 GMT
Article-I.D.: midway.1992Sep29.142601.9338
Reply-To: pcp1@midway.uchicago.edu
Organization: University of Chicago Computing Organizations

I was wondering what the statistical community thinks of the
Sequential Bonferroni test proposed by Holm (1979 Scand
J Statist 6:65-70).  This adjustment attempts to control
Type 1 errors much in the same way that the standard
Bonferroni method, except that it is much more lenient
(powerful).  It works like this:

In an ordinary Bonferroni adjustment, the comparisonwise
error rate (alpha[ind]) is controlled by dividing the
experimentwise error by the number of comparisons to be
made (alpha[exp]/n).  It is known that this tends to 
conservatively set the true experimentwise error <= alpha[exp].

In the sequential Bonferroni method, much the same adjustment
is made, but instead of testing all comparisons at the
alpha[exp]/n level, you only divide alpha[exp] by the number
of tests (or comparisons) remaining.  As soon as you reach
a non-significant result, however, you stop testing.

For example, if the p-values for five different tests were
  0.008 0.01 0.012 0.02 0.04   setting alpha[exp] = 0.05
only the first two tests would be significant by the
standard Bonferroni criteria, but all tests would be
significant by the sequential Bonferroni method.

What do statisticians think of this method?  Predictably, it
has not made it into any text books that I have seen.  It is
becoming popular among population biologists because of a
paper by Rice (1989 Evolution 43:223-225).  Is it OK to use?

Patrick Phillips
University of Texas at Arlington
phil@decster.uta.edu
Newsgroups: sci.math.stat
From: behzad@ec.ecn.purdue.edu (Behzad M Shahshahani)
Subject: expected value of the esimated covariance matrix
Organization: Purdue University Engineering Computer Network
Date: Fri, 2 Oct 1992 16:36:28 GMT

This message didnot go through:

I forgot to mention that I am interested in the multivariate case.
Therefore here is my question again:
Assuming that the covariance matrix is estimated from a set of
multivariate normal observation, and the usual unbiased estimator S is
used, then what is the expected value of the inverse of matrix S ?

-behzad
From: bill@bessel.as.utexas.edu (William H. Jefferys)
Newsgroups: sci.math.stat
Subject: Re: Let's Make a Deal!
Date: 29 Sep 92 03:40:24 GMT
Organization: McDonald Observatory, University of Texas @ Austin

In article <1992Sep24.222349.7583@u.washington.edu> rons@hardy.u.washington.edu (Ronald Schoenberg) writes:
#
#My view, and probably Savant's view, is that the discussion about the
#problem being ill-posed is really a way of avoiding admitting that a
#lot of mathematicians and statisticians screwed up a problem that she
#got right.  Did all those Ph.D. mathematicians and statisticians who
#sent the wrong answers while patronizing her and treating her like a
#child because she didn't have a Ph.D. (or possibly also because she
#was a woman) get the problem wrong because it was ill-posed?  No, they
#got it wrong because they screwed up in spite of their Ph.D.'s.  And
#now they want to deny that they screwed up by saying that it was
#ill-posed.  Maybe a few got it wrong because they saw a different
#problem, but I don't think very many.  And I think the proper response
#to Savant should have been a little respect instead of some technical
#diversion to slake some male egos.

Well, no. After her first article was published, I
wrote her and pointed out that (1) her problem was
ill-posed, but (2) if posed more carefully (it only
required changing a few words, for God's sake) then
she was correct. I never received the courtesy of
a reply. I think there is more than mathematicians'
and statisticians' egos involved. MvS's ego is
equally involved, and her response to a critical
article in (what?) Journal of Mathematical
Education (?)--the reference was posted recently--
shows this very clearly.)

Bill

Newsgroups: sci.math.stat
From: packer@amarna.gsfc.nasa.gov (Charles Packer)
Subject: Re: Let's Make a Deal!
News-Software: VAX/VMS VNEWS 1.4-b1  
Organization: Dept. of Independence
Date: Mon, 28 Sep 1992 10:38:00 GMT

Wait a minute, I thought I understood this a few months
ago when it was discussed here. But now I write down what
I understand to be the sample space and it says: don't
bother to switch, it's a fair game:

Assume for simplification that the prize is always behind door A.
Probabiliy of each outcome is listed:
I choose A, host chooses B, I stay with A 1/12 win
I choose A, host chooses B, I switch to C 1/12
I choose A, host chooses C, I stay with A 1/12 win
I choose A, host chooses C, I switch to B 1/12
I choose B, host chooses C, I stay with B 1/6
I choose B, host chooses C, I switch to A 1/6  win
I choose C, host chooses B, I stay with C 1/6
I choose C, host chooses B, I switch to A 1.6  win

Add the "win" probabilities together, and they come out
to .5. I must have forgotten something...
From: statlibd@stat.cmu.edu (Statlib Server)
Newsgroups: sci.math.stat
Subject: New StatLib entries for September 1992
Date: 6 Oct 92 10:08:37 GMT
Article-I.D.: temper.9210061808.AA23660
Organization: Carnegie Mellon, Pittsburgh, PA

The following is a short list of the new or modified StatLib entries
for September 1992 (until 6 October 1992).

S		gee	
		fracdiff	# Update
		bivsurv		# Update
		logspline	# Update
		translate	# Update
		doshist		# A history function for DOS
		lmix		# Linear mixed effects models
		latex.table	# Convert S table into LaTeX

apstat		190		# Bug fix

s-news		No new digests  :-(

datasets	profb		# points and spreads for NFL games 89-91


general		fracdiff	# Update
		logist		# Update
		cissearch	# Code for search the CIS


jcgs		aragon.eberly   # Damped Convex Minorant Algorithm
		jiang-k-d	# Carlson's Multiple Hypergeometric Function R

jqt		24-4		# Owen's Q Function


To get started using StatLib, send the one line message
	send index
to statlib@lib.stat.cmu.edu

StatLib also has an anonymous FTP facility.
Use ftp to connect to lib.stat.cmu.edu and login with user name
"statlib".  PLEASE send your e-mail address as your password.  This
will enable us to notify you of changes to the software and notify the
contributors about who has requested the software.

A reminder that there is also an archive of SAS related material.
Send the message
	send sas from general
for details about the SAS-L archive.

Thanks to all the contributors -- please keep the contributions
coming! 

Mike Meyer, Carnegie Mellon.

================================================================
Newsgroups: sci.math.stat,sci.math.num-analysis
From: charlie@umnstat.stat.umn.edu (Charles Geyer)
Subject: Re: Gibbs sampling and annealing (was: Help!  Book recommendations?)
Organization: School of Statistics, University of Minnesota
Date: Tue, 6 Oct 1992 22:52:31 GMT

In article <1992Oct5.225003.11916@news2.cis.umn.edu> I wrote:

  [Annealing] doesn't help much.  All it attempts to do is produce one
  realization that is "almost" from the stationary distribution to
  use as the start of the run.

  But if the chain mixes very slowly, the average will be near the starting
  point, but not near the expectation one wants to calculate.

  On the other hand, if the chain mixes rapidly, the "annealing" isn't
  needed.

In article <92Oct6.115856edt.512@neuron.ai.toronto.edu> radford@cs.toronto.edu
(Radford Neal) writes:

> Not necessarily. It could be that the chain mixes rapidly once it has
> reached the right general region, but that without annealing it takes
> a long time to reach that region from whereever it was started.

Possible, in which case I'll concede that annealing to find starting
points is a good idea.

-- 
Charles Geyer
School of Statistics
University of Minnesota
charlie@umnstat.stat.umn.edu
From: dantonio@iiic.ethz.ch (Denis Nicolas Antonioli)
Newsgroups: sci.math.stat
Subject: A question of history...
Date: 28 Sep 92 18:36:25 GMT
Organization: Dept. Informatik, Swiss Federal Institute of Technology (ETH), Zurich, CH

Hi,
I crossed this name a few time, but never got any historical references, so...

Who was (is ?):
	>> BONFERRONI <<
known by its inequality and the mutliple comparisons procedures.

Thanks for any hints.
                  dantonio@iiic.ethz.ch
From: olevin@random.hwr.arizona.edu
Newsgroups: sci.math.stat
Subject: Re: Principle Components Analysis
Date: 3 Oct 92 15:54:24 GMT
Distribution: na
Organization: School of Engineering and Applied Sciences, UCLA

In article <BvIqDK.9EK@csn.org> sullivan@teal.csn.org (Steve Sullivan) writes:
>
>I'm interested in learning about principle components
>analysis, but none of the profs I've talked to have
>any references.
>
>Can anyone recommend some good introductory books or
>articles?
>
>Many thanks in advance -
>
>Steve Sullivan
>sullivan@csn.org

Cattell, r.b. (965) Factor analysis. An introduction to
Essentials. I: the purpose and underlying models. Biometrics 21,
pp. 190-210
II: the role of factor analysis in research, Biometrics, 21,
pp405-435.

Childs d 1970, The essentials of factor analysis. London, Holt,
Rinehart, and Wisnton.

Cooley W.W> and Lohnes (1971) Multivariate data analysis, NY
J-Wiley.

Davis, J./c. 1973) Statistics and data analyiss i geology,
Academic press

Fruchter b. (1954) /Introduction to factor analysis D. van
Nostrand Co.

Seyhan E. 1985 Introduction to multivariate statistical analysis
Free university, Institute of Earth sciences Netherlands


Oscar
Newsgroups: sci.math.stat
Newsgroups: sci.math.stat,sci.math.num-analysis
From: radford@cs.toronto.edu (Radford Neal)
Subject: Re: Gibbs sampling and annealing (was: Help!  Book recommendations?)
Organization: Department of Computer Science, University of Toronto
Date: 4 Oct 92 17:10:10 GMT

In article <1992Oct3.210337.21498@news2.cis.umn.edu> charlie@umnstat.stat.umn.edu (Charles Geyer) writes:

> It makes no sense to compare Gibbs sampling versus simulated annealing, 
> for example.

Correct, but not for the reasons you perhaps mean. Simulated annealing
is a technique that can be used in conjunction with Gibbs sampling 
(or other Markov chain methods, such as the Metropolis algorithm) 
in order to reach the final equilibrium distribution more quickly.

Most of the annealing literature is devoted to the case of annealing down
to a "temperature" of zero, where the distribution being sampled from
is concentrated at the points where the global minimum is realized, but
the technique is equally applicable when the goal is to sample from the
distribution at some non-zero temperature. 

Gibbs sampling and simulated annealing have been used in this fashion with
the "Boltzmann machine" neural network of Ackley, Hinton, and Sejnowski
(Ccognitive Science, vol. 9, pp. 147-169, 1985), and for many simulations 
of physical systems.

    Radford Neal
Newsgroups: sci.math.stat,sci.math.num-analysis
From: charlie@umnstat.stat.umn.edu (Charles Geyer)
Subject: Re: Gibbs sampling and annealing (was: Help!  Book recommendations?)
Organization: School of Statistics, University of Minnesota
Date: Sun, 4 Oct 1992 19:22:17 GMT

In article <1992Oct3.210337.21498@news2.cis.umn.edu> I wrote:

   It makes no sense to compare Gibbs sampling versus simulated annealing, 
   for example.

In article <92Oct4.130952edt.243@neuron.ai.toronto.edu> radford@cs.toronto.edu
(Radford Neal) replies:

> Correct, but not for the reasons you perhaps mean. Simulated annealing
> is a technique that can be used in conjunction with Gibbs sampling 
> (or other Markov chain methods, such as the Metropolis algorithm) 
> in order to reach the final equilibrium distribution more quickly.

Depends on how you think about Markov chain Monte Carlo.  The
Metropolis-Hastings algorithm produces a Markov chain with a known
stationary distribution.  This "annealing trick" doesn't.  So far
from mixing MCMC and simulated annealing, it is not MCMC at all.

Are there any practically useful convergence results for such schemes
once Markov chain theory is thrown out the window?

-- 
Charles Geyer
School of Statistics
University of Minnesota
charlie@umnstat.stat.umn.edu
Xref: sparky sci.math.stat:2064 sci.math.num-analysis:2920
From: retzer@convex.csd.uwm.edu (Joe J Retzer)
Newsgroups: sci.math.stat,sci.math.num-analysis
Subject: Gauss for windows
Date: 4 Oct 1992 22:31:26 GMT
Organization: University of Wisconsin - Milwaukee
Distribution: inet
Reply-To: retzer@convex.csd.uwm.edu
NNTP-Posting-Host: 129.89.7.30
Originator: retzer@convex.csd.uwm.edu

Does anyone know if Aptech is planning to come out with a version of 
Gauss for windows? I'd really hate to learn another package if I can
avoid it, but I'll make a switch if necessary.
	
			   Many thanks in advance,
				   Joe
From: kt14+@andrew.cmu.edu (Kok Hooi Tan)
Newsgroups: sci.math.stat
Subject: DIEHARD test of randomness
Date: 30 Sep 92 09:25:32 GMT
Article-I.D.: andrew.kemSAAa00j61Exs0IC
Organization: Doctoral student, Statistics, Carnegie Mellon, Pittsburgh, PA


 Hello,
 Are the codes of the so called DIEHARD tests of random numbers by Marsaglia 
 publicly available, say in some ftp site ?
 Thanks,

---------------------------
Kok Hooi Tan                    
Dept of Statistics              
Carnegie Mellon University      
Pittsburgh, PA 15213            
---------------------------
From: rossini@biosun4.harvard.edu (Anthony Rossini)
Newsgroups: sci.math.stat
Subject: Re: UMVU estimation for infinite-dimension models
Date: 29 Sep 92 21:07:20 GMT
Distribution: usa
Organization: Biostat Dept, HSPH, Boston MA, USA.
In-reply-to: thompson@atlas.socsci.umn.edu's message of 29 Sep 92 19:15:34 GMT


Correct me if I am wrong, but when you say non-parametric, are you excluding
semi-parametric models as well?  If you are looking at strictly
non-parametric estimators, such as estimators for distribution functions,
then the asymptotics (which sometimes result in funky cube-root things)
might result in Rao-Blackwell type theorems, but not easily understandable
ones (at least to me, so far).

-tony


--
Anthony Rossini       -       rossini@biostat.harvard.edu
Department of Biostatistics, Harvard School of Public Health
677 Huntington Ave, Boston MA 02115  617-432-1056
Newsgroups: sci.math.stat
From: thompson@atlas.socsci.umn.edu (T. Scott Thompson)
Subject: Re: UMVU estimation for infinite-dimension models
Reply-To: thompson@atlas.socsci.umn.edu
Organization: Economics Department, University of Minnesota
Date: Wed, 30 Sep 1992 16:23:02 GMT

rossini@biosun4.harvard.edu (Anthony Rossini) writes:

>Correct me if I am wrong, but when you say non-parametric, are you excluding
>semi-parametric models as well?  If you are looking at strictly
>non-parametric estimators, such as estimators for distribution functions,
>then the asymptotics (which sometimes result in funky cube-root things)
>might result in Rao-Blackwell type theorems, but not easily understandable
>ones (at least to me, so far).

I'm not quite sure how to answer this since I have yet to hear a
definition of "semiparametric" that I am wholly happy with.  What I
would like is results for estimation of a finite vector of functionals
of the distribution in a case where the distribution is known to lie
in some infinite-dimensional set.  So the estimand is finite
dimensional but the probability model is not.  If this is what you
mean by semiparametric then that is what I want.  The simplest example
that I can think of would be estimation of the mean of a distribution
for which it is only known that first and second moments exist.  Some
would call this a nonparametric problem, however.

I would also like to be able to handle problems where the probability
model is restricted in a more substantial way by qualitative
restrictions, but not so much as to admit a finite dimensional
parameterization.

An example of the latter form that I have given some thought to is:

Suppose that we observe and i.i.d. sample of (x,y) that are known to
be related by the equaiton y = x'b, where b is a random vector of the
same dimension as x that is assumed to be distributed independently of
x.  Assume only

           y = x'b
           b is independent of x
           E(x'x) and E(b'b) are finite.

These assumptions substantially restrict the distribution of (y,x),
but the model is nevertheless nonparametric.  Now suppose that I want
to find the UMVU estimator for E(b).  This is a problem that I am
actually working on at the moment.

The semiparametric efficiency bounds literature can be used to show
that a generalized least squares estimator (or a feasible generalized
least squares estimator) is asymptotically efficient.  The
Gauss-Markov theorem tells us that the GLS estimator is best among all
_linear_ uniformly unbiased estimators in a finite sample.  But I
don't know of any results showing that the GLS estimator is best among
all uniformly unbiased estimators in a finite sample.

-- Scott
--
T. Scott Thompson              email:  thompson@atlas.socsci.umn.edu
Department of Economics        phone:  (612) 625-0119
University of Minnesota        fax:    (612) 624-0209