paper available

[Ralph Neumeier]

FTP-host: archive.cis.ohio-state.edu
FTP-filename: /pub/neuroprose/.ps.Z


The following paper, which is closely related to the two 
recently announced paper of Chris. M. Bishop and
Z. Ghahramani on density estimation is now available 
by anonymous ftp (7 pages, no hardcopies).
It will appear in the proceedings of ICANN'94 
(INTERNATIONAL CONFERENCE ON ARTIFICIAL NEURAL NETWORKS)
Springer-Verlag London Ltd
Any questions or comments will be highly appreciated.
E-mail: Ralph.Neuneier at zfe.siemens.de

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	Estimation of Conditional Densities:
	A Comparison of Neural Network Approaches


R.Neuneier, 
F.Hergert,
 Siemens AG, Corporate Research and Development, 
 D-81730 Munich, Germany
W.Finnoff, Prediction Company, Santa Fe, NM 8750
D.Ormoneit, Dept. of CS, TUM, D-80290 Munich, Germany

ABSTRACT: We present a comparison of various network 
architectures and learning algorithms (EM, Gradient Descent) 
to estimate conditional densities p(y|x) for future values 
of time series given past observations. There are two principal 
ways to approach this problem: Either one can estimate the 
conditional density directly, or first compute the joint 
densities p(x,y) and subsequently derive the conditional 
density p(y|x) from p(x,y). We compared the performance of 
both approaches using a bounded Brownian process and real 
exchange rates (U.S.$-SFR). In our experiments, the direct 
approach turned out to be superior.
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             Instruction for retrieving this paper:

ftp archive.cis.ohio-state.edu
Name: anonymous
password: your full email address
ftp> cd pub/neuroprose
ftp> binary
ftp> get neuneier.cond_dens.ps.Z
ftp> bye
uncompress neuneier.cond_dens.ps.Z
lpr neuneier.cond_dens.ps
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Ralph Neuneier
ZFE ST SN 41
Siemens AG
Otto-Hahn-Ring 6
D 81730 Muenchen, Germany

Phone: +49/89/636-49506 Fax:   +49/89/636-3320

e-mail: Ralph.Neuneier at zfe.siemens.de