Neapolitan,
richard.neapoli...@northwestern.edu.__
--
Richard E. Neapolitan, Ph.D.
Division of Biomedical Informatics
Department of Preventive Medicine
Northwestern Feinberg School of Medicine
750 N. Lake Shore Drive, 11th Floor
Chicago, Illinois 60611
I have a dataset with about 10,000 records and about 20 attributes.
However, some records only have data on a few of the attributes. A
search revealed no formal way for deciding when to simply delete the
record. They only say that in "case deletion" we simply delete the
record if too many field
various values must be tried. However, based on the
literature I have so far found, I can't even formulate ranges for my
particular problem.
Thanks,
Rich
--
Richard E. Neapolitan, Ph.D.
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Can anyone recommend software for learning and reasoning with an Augmented
Naive Bayesian Network?
Thanks,
Rich
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I am looking for a simple available software package that builds (not learns) a
BN stucture. That is,
Input:
1. Set of nodes and number of states of each node.
2. Set of edges
Output:
A BN DAG Model.
Thanks,
Rich
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continued demand for the book, I have made
arrangements to bring it back into print. New paperback copies can be
purchased at the following site:
https://www.createspace.com/3875541
Best regards,
Rich
Richard E. Neapolitan
Professor of Computer Science
Northeastern Illinois University
5500 N
My new artificial intelligence textbook Contemporary Artificial
Intelligence is now available. I wrote this book largely because no
current text was accessible to students at a mainstream university
like Northeastern Illinois University. I had this same problem with
my analysis of algorithms co
searchalgorithms, in a particular ones implemented in
available software, that address this situation. Clearly, there are
modifcations of these algorithms that would do so.
Thanks,
Rich
--
Richard E. Neapolitan, Ph.D., Professor
Division of Health and Biomedical Informatics
Department of Preventive
Dear Colleagues,
Once again, this is not a job ad. I am interested in learning of recent
BN learning packages that allow for both continuous and discrete variables.
Best,
Rich
--
Richard E. Neapolitan, Ph.D., Professor
Division of Health and Biomedical Informatics
Department of Preventive
I am interested in learning of methods for learning the parameters in a
Bayesian networks from several datasets, each of which contains data on
the variables in the network.
Thanks,
Rich
--
Richard E. Neapolitan, Professor
Division of Biomedical Informatics
Department of Preventive Medicine
I am looking for an accessible software package that implements
approximate modelaveraging using MCMC as presented in my 2004 text
Learning Bayesian networks. Pleaseinform me if you know of anything.
Thanks,
Rich
--
Richard E. Neapolitan, Professor
Division of Biomedical Informatics
Department
is no research that
gives me any reason to believe this. In my recent AI textbook I took the
stance that we have essentially failed at this endeavor. Does anyone
know of anyresearch that would make someone make such a statement?
Thanks,
Rich
--
Richard E. Neapolitan, Ph.D., Professor
Divisio
, Robert L.
To: Richard E. Neapolitan
Firstly, we have collectively failed at this endeavor so far. Unlike
the past,
however, there are clearly ongoing research threads that imply that
the 10 to 100 year is reasonable. This is of course an opinion based
on my own research and views
of it. The only thing I currently see is perhaps an artificially grown
brain modeled after the way the human brain grows. But that would be
artificial life rather than intelligence.
Richard E. Neapolitan, Professor
Division of Biomedical Informatics
Department of Preventive Medicine
Northwestern
concerned
establishing criteria for determining whether an entity is intelligent.
I have not investigated this matter myself. Maybe this book has
something to say.
Richard E. Neapolitan, Professor
Division of Biomedical Informatics
Department of Preventive Medicine
Northwestern University Feinberg School o
A number of applications involves learning the probability of edges in
Bayesian networks using techniques such as MCMC or weighting over
different sources. I am looking for inference algorithms that take into
account the weights of the edges.
--
Richard E. Neapolitan, Professor
Division of
Dear Colleagues,
I am looking for softwarethat computes the probability that an edge is
present in the DAG using approximate model averaging as described in my
Learning Bayesian Network text, or something similar. I would appreciate
any information.
Best,
Rich
--
Richard E. Neapolitan, Ph.D
--
Richard E. Neapolitan, Ph.D., Professor
Division of Health and Biomedical Informatics
Department of Preventive Medicine
Northwestern University Feinberg School of Medicine
750 N. Lake Shore Drive, 11th floor
Chicago IL 60611
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ted for so long, and more is not said about
them.
Best,
Rich
--
Richard E. Neapolitan, Ph.D., Professor
Division of Health and Biomedical Informatics
Department of Preventive Medicine
Northwestern University Feinberg School of Medicine
750 N. Lake Shore Drive, 11th floor
Chicag
dergrad stats from a
Bayesian POV is this:
@book{Kruschke10,
title = {{Doing Bayesian Data Analysis: A Tutorial Introduction with
R and
BUGS}},
author = "J. Kruschke",
year = 2010,
publisher = "Academic Press"
}
On Fri, Sep 26, 2014 at 1:59 PM, Richard E Neapol
Yes, I remember well Zadeh and Cheeseman arguing in the old glory days of the
Uncertainty in AI Workshop. He will be missed.
Richard Neapolitan
Professor
Biomedical Informatics
From: uai [mailto:uai-boun...@engr.orst.edu] On Behalf Of Kathryn B. Laskey
Sent: Thursday, September 07, 2017 9:51 AM
I always was with Zadeh in the fuzzy set theory versus probability
arguments. I noted that fuzzy set theory is not probability theory under
certain assumptions as some argued (e.g. at the special UAI working on
higher order uncertainty in the 90s at George Mason University). Rather
fuzzy set th
My new textbook /Artificial Intelligence: With an Introduction to
Machine Learning/ will appear in March, 2018 (It is actually a second
edition of a text with a different name). The book has 5 parts:
1. Logical Intelligence
2. Probabilistic Intelligence (Bayesian networks)
3. Emergent Intellige
Netica
Richard Neapolitan
Professor
Biomedical Informatics
-Original Message-
From: uai [mailto:uai-boun...@engr.orst.edu] On Behalf Of Robert Goldman
Sent: Friday, January 26, 2018 3:14 PM
To: uai@engr.orst.edu
Subject: [UAI] Influence diagram "calculator"?
Can anyone point me at some f
]
Sent: Sunday, January 28, 2018 11:42 AM
To: Richard E Neapolitan
Cc: uai@engr.orst.edu
Subject: Re: [UAI] Influence diagram "calculator"?
This seems like a useful choice: it's commercial, but provides a limited trial
license for free. Very appealing, since if it worked out for me o
My text "Contemporary Artificial Intelligence: With an Introduction to
Machine Learning" is now in print. The text is meant for an
undergraduate course in artificial intelligence, which would follow or
be concurrent with a data structures course. There are 5 parts:
1. Logical Intelligence
2
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