Next: About this document ...
Up: AIS-BN: An Adaptive Importance
Previous: Acknowledgements
- Cano et al.1996
-
Jose E. Cano, Luis D. Hernandez, and Serafin Moral.
Importance sampling algorithms for the propagation of probabilities
in belief networks.
International Journal of Approximate Reasoning, 15:77-92,
1996.
- Chavez and Cooper1990
-
Martin R. Chavez and Gregory F. Cooper.
A randomized approximation algorithm for probabilistic inference on
Bayesian belief networks.
Networks, 20(5):661-685, August 1990.
- Cheng and Druzdzel2000a
-
Jian Cheng and Marek J. Druzdzel.
Computational investigations of low-discrepancy sequences in
simulation algorithms for Bayesian networks.
In Proceedings of the Sixteenth Annual Conference on Uncertainty
in Artificial Intelligence (UAI-2000), pages 72-81, San Francisco, CA,
2000. Morgan Kaufmann Publishers.
- Cheng and Druzdzel2000b
-
Jian Cheng and Marek J. Druzdzel.
Latin hypercube sampling in Bayesian networks.
In Proceedings of the 13th International Florida Artificial
Intelligence Research Symposium Conference (FLAIRS-2000), pages 287-292,
Orlando, Florida, May 2000.
- Conati et al.1997
-
Cristina Conati, Abigail S. Gertner, Kurt VanLehn, and Marek J. Druzdzel.
On-line student modeling for coached problem solving using Bayesian
networks.
In Proceedings of the Sixth International Conference on User
Modeling (UM-96), pages 231-242, Vienna, New York, 1997. Springer Verlag.
- Cooper1990
-
Gregory F. Cooper.
The computational complexity of probabilistic inference using
Bayesian belief networks.
Artificial Intelligence, 42(2-3):393-405, March 1990.
- Cousins et al.1993
-
Steve B. Cousins, William Chen, and Mark E. Frisse.
A tutorial introduction to stochastic simulation algorithm for belief
networks.
In Artificial Intelligence in Medicine, chapter 5, pages
315-340. Elsevier Science Publishers B.V., 1993.
- Dagum and Luby1993
-
Paul Dagum and Michael Luby.
Approximating probabilistic inference in Bayesian belief networks
is NP-hard.
Artificial Intelligence, 60(1):141-153, 1993.
- Dagum and Luby1997
-
Paul Dagum and Michael Luby.
An optimal approximation algorithm for Bayesian inference.
Artificial Intelligence, 93:1-27, 1997.
- Dagum et al.1995
-
Paul Dagum, Richard Karp, Michael Luby, and Sheldon Ross.
An optimal algorithm for Monte Carlo estimation (extended
abstract).
In Proceedings of the 36th IEEE Symposium on Foundations of
Computer Science, pages 142-149, Portland, Oregon, 1995.
- Diez1993
-
Francisco Javier Diez.
Parameter adjustment in Bayes networks. The generalized noisy
OR-gate.
In Proceedings of the Ninth Annual Conference on Uncertainty in
Artificial Intelligence (UAI-93), pages 99-105, San Francisco, CA, 1993.
Morgan Kaufmann Publishers.
- Fishman1995
-
George S. Fishman.
Monte Carlo: concepts, algorithms, and applications.
Springer-Verlag, 1995.
- Fung and Chang1989
-
Robert Fung and Kuo-Chu Chang.
Weighing and integrating evidence for stochastic simulation in
Bayesian networks.
In Uncertainty in Artificial Intelligence 5, pages 209-219,
New York, N. Y., 1989. Elsevier Science Publishing Company, Inc.
- Fung and del Favero1994
-
Robert Fung and Brendan del Favero.
Backward simulation in Bayesian networks.
In Proceedings of the Tenth Annual Conference on Uncertainty in
Artificial Intelligence (UAI-94), pages 227-234, San Francisco, CA, 1994.
Morgan Kaufmann Publishers.
- Geman and Geman1984
-
S. Geman and D. Geman.
Stochastic relaxations, Gibbs distributions and the Bayesian
restoration of images.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
6(6):721-742, 1984.
- Gilks et al.1996
-
W. Gilks, S. Richardson, and D. Spiegelhalter.
Markov chain Monte Carlo in practice.
Chapman and Hall, 1996.
- Heckerman and Breese1994
-
David Heckerman and John S. Breese.
A new look at causal independence.
In Proceedings of the Tenth Annual Conference on Uncertainty in
Artificial Intelligence (UAI-94), pages 286-292, San Mateo, CA, 1994.
Morgan Kaufmann Publishers, Inc.
- Heckerman et al.1990
-
David E. Heckerman, Eric J. Horvitz, and Bharat N. Nathwani.
Toward normative expert systems: The Pathfinder project.
Technical Report KSL-90-08, Medical Computer Science Group, Section
on Medical Informatics, Stanford University, Stanford, CA, February 1990.
- Henrion1988
-
Max Henrion.
Propagating uncertainty in Bayesian networks by probabilistic logic
sampling.
In Uncertainty in Artificial Intellgience 2, pages 149-163,
New York, N. Y., 1988. Elsevier Science Publishing Company, Inc.
- Henrion1989
-
Max Henrion.
Some practical issues in constructing belief networks.
In L.N. Kanal, T.S. Levitt, and J.F. Lemmer, editors, Uncertainty in Artificial Intelligence 3, pages 161-173. Elsevier Science
Publishers B.V., North Holland, 1989.
- Henrion1991
-
Max Henrion.
Search-based methods to bound diagnostic probabilities in very large
belief nets.
In Proceedings of the Seventh Annual Conference on Uncertainty
in Artificial Intelligence (UAI-91), pages 142-150, San Mateo, California,
1991. Morgan Kaufmann Publishers.
- Hernandez et al.1998
-
Luis D. Hernandez, Serafin Moral, and Salmeron Antonio.
A Monte Carlo algorithm for probabilistic propagation in belief
networks based on importance sampling and stratified simulation techniques.
International Journal of Approximate Reasoning, 18:53-91,
1998.
- Jacobs1988
-
Robert A. Jacobs.
Increased rates of convergence through learning rate adaptation.
Neural Networks, 1:295-307, 1988.
- Lauritzen and
Spiegelhalter1988
-
Steffen L. Lauritzen and David J. Spiegelhalter.
Local computations with probabilities on graphical structures and
their application to expert systems.
Journal of the Royal Statistical Society, Series B
(Methodological), 50(2):157-224, 1988.
- MacKay1998
-
D. MacKay.
Intro to Monte Carlo methods.
In Michael I. Jordan, editor, Learning in Graphical Models. The
MIT Press, Cambridge, Massachusetts, 1998.
- Ortiz and Kaelbling2000
-
Luis E. Ortiz and Leslie Pack Kaelbling.
Adaptive importance sampling for estimation in structured domains.
In Proceedings of the Sixteenth Annual Conference on Uncertainty
in Artificial Intelligence (UAI-2000), pages 446-454, San Francisco, CA,
2000. Morgan Kaufmann Publishers.
- Pearl1986
-
Judea Pearl.
Fusion, propagation, and structuring in belief networks.
Artificial Intelligence, 29(3):241-288, September 1986.
- Pearl1987
-
Judea Pearl.
Evidential reasoning using stochastic simulation of causal models.
Artifical Intelligence, 32:245-257, 1987.
- Pearl1988
-
Judea Pearl.
Probabilistic Reasoning in Intelligent Systems: Networks of
Plausible Inference.
Morgan Kaufmann Publishers, Inc., San Mateo, CA, 1988.
- Pradhan and Dagum1996
-
Malcolm Pradhan and Paul Dagum.
Optimal Monte Carlo inference.
In Proceedings of the Twelfth Annual Conference on Uncertainty
in Artificial Intelligence (UAI-96), pages 446-453, San Francisco, CA,
1996. Morgan Kaufmann Publishers.
- Pradhan et al.1994
-
Malcolm Pradhan, Gregory Provan, Blackford Middleton, and Max Henrion.
Knowledge engineering for large belief networks.
In Proceedings of the Tenth Annual Conference on Uncertainty in
Artificial Intelligence (UAI-94), pages 484-490, San Francisco, CA, 1994.
Morgan Kaufmann Publishers.
- Ritter et al.1991
-
H.J. Ritter, T.M. Martinetz, and K.J. Schulten.
Neuronale Netze.
Addison-Wesley, München, 1991.
- Rubinstein1981
-
Reuven Y. Rubinstein.
Simulation and the Monte Carlo Method.
John Wiley & Sons, 1981.
- Seroussi and Golmard1994
-
B. Seroussi and J. L. Golmard.
An algorithm directly finding the K most probable configurations in
Bayesian networks.
International Journal of Approximate Reasoning, 11:205-233,
1994.
- Shachter and Peot1989
-
Ross D. Shachter and Mark A. Peot.
Simulation approaches to general probabilistic inference on belief
networks.
In Uncertainty in Artificial Intelligence 5, pages 221-231,
New York, N. Y., 1989. Elsevier Science Publishing Company, Inc.
- Shwe and Cooper1991
-
M. A. Shwe and G. F. Cooper.
An empirical analysis of likelihood-weighting simulation on a large,
multiply-connected medical belief network.
Computers and Biomedical Research, 24(5):453-475, 1991.
- Shwe et al.1991
-
M.A. Shwe, B. Middleton, D.E. Heckerman, M. Henrion, E.J. Horvitz, and H.P.
Lehmann.
Probabilistic diagnosis using a reformulation of the
INTERNIST-1/QMR knowledge base: I. The probabilistic model and
inference algorithms.
Methods of Information in Medicine, 30(4):241-255, MONTH 1991.
- Srinivas1993
-
Sampath Srinivas.
A generalization of the noisy-OR model.
In Proceedings of the Ninth Annual Conference on Uncertainty in
Artificial Intelligence (UAI-93), pages 208-215, San Francisco, CA, 1993.
Morgan Kaufmann Publishers.
- York1992
-
Jeremy York.
Use of the Gibbs sampler in expert systems.
Artificial Intelligence, 56:115-130, 1992.
Jian Cheng
2000-10-01