Book:
·
N.L. Zhang and H.P. Guo (2006). Introduction to Bayesian Networks.
Science Press, Beijing. Lecture notes based on the book.
Journal Articles
1.
A. H. Liu, Leonard K.M. Poon, T.F. Liu, N. L. Zhang
(2014).
Latent tree models for rounding in spectral clustering. Neurocomputing, Volume 144, 20 November
2014, Pages 448–462
2. Y
Zhao, NL Zhang, T Wang, Q Wang (2014), Discovering
Symptom Co-Occurrence Patterns from 604 Cases of Depressive Patient Data Using
Latent Tree Models, The Journal of Alternative and Complementary Medicine. doi:10.1089/acm.2013.0178.
January 20, 2014.
3. T.F,
Liu, N. L. Zhang, P. X. Chen, A. H.Liu, L. K. M. Poon, and Yi Wang (2013). Greedy
learning of latent tree models for multidimensional clustering. Machine Learning, doi:10.1007/s10994-013-5393-0.
4.
R. Mourad, C. Sinoquet, N. L. Zhang, T.F. Liu and
P. Leray (2013). A survey
on latent tree models and applications. Journal of
Artificial Intelligence Research, 47, 157-203 , 13 May
2013. doi:10.1613/jair.3879
5.
L.K.M. Poon, N.L. Zhang, T.F. Liu, A.H. Liu (2013). Model-Based
Clustering of High-Dimensional Data: Variable Selection versus Facet
Determination. International
Journal of Approximate Reasoning. 54(1), 196-215. http://dx.doi.org/10.1016/j.ijar.2012.08.001
6.
Y. Wang, N. L. Zhang, T. Chen, L.K.M. Poon
(2013). LTC:
A latent tree approach to classification. International Journal of Approximate Reasoning. 54(4),
560-572. http://dx.doi.org/10.1016/j.ijar.2012.06.024.
7.
Z.X. Xu, N.
L. Zhang, Y.Q. Wang, G.P. Liu, J. Xu, T. F. Liu, and A. H. Liu (2013). Statistical
Validation of Traditional Chinese Medicine Syndrome Postulates in the Context
of Patients with Cardiovascular Disease. The Journal of Alternative and Complementary Medicine. 18, 1-6. http://online.liebertpub.com/doi/abs/10.1089/acm.2012.0487
8.
T. Chen, N. L. Zhang, T. F. Liu, Y.
Wang, L. K. M. Poon (2012). Model-based
multidimensional clustering of categorical data. Artificial
Intelligence, 176(1), 2246-2269. doi:10.1016/j.artint.2011.09.003.
9.
N. L. Zhang, S.
H. Yuan, T. F. Wang, et al. (2011). Latent
Structure Analysis and Syndrome Differentiation for the Integration of
Traditional Chinese and Western Medicine (I): Basic Principle. World Science and Technology-Modernization of Traditional
Chinese Medicine and Materia Materia. 13(3),
498-502.
10. N.
L. Zhang,
Z. X. Xu, Y. Q. Wang, et al. (2012). Latent
Structure Analysis and Syndrome Differentiation for the Integration of
Traditional Chinese and Western Medicine (II): Joint Clustering. World Science and Technology-Modernization of Traditional
Chinese Medicine and Materia Materia. Accepted.
11. S.
H. Yuan, T. F. Wang, N. L. Zhang (2011). The Inception
of TCM Syndrome Types in Human Cognitation and Dataming Methods for TCM Data. Journal of Traditional Chinese Medicine. Vol 52, No 4, 284-287.
12. T.
F. Wang, N. L. Zhang, Y. Zhao, Y. Wang et al. (2009). Latent
structure models and their applications in TCM syndrome research. Journal
of Beijing University of Chinese Medicine,32(8):519-526.
13. Y. B. Gong, N. L. Zhang,
S. H. Gao, et al (2009). A study of symptom
distribution regularities of Type II Diabetes using latent structure models.
World Science and Technology-Modernization of
Traditional Chinese Medicine and Materia Materia. 4(4): 516-521.
14. Y.
Wang, N. L. Zhang and T. Chen (2008). Latent tree
models and approximate inference in Bayesian networks. Journal of
Artificial Intelligence Research, 32, 879-900.
15. N.
L. Zhang,
S. H. Yuan, T. Chen and Y. Wang (2008). Statistical
Validation of TCM Theories. Journal of Alternative and Complementary
Medicine, 14(5):583-7.
16. N.
L. Zhang,
S. H. Yuan, T. Chen and Y. Wang (2008). Latent tree models and
diagnosis in traditional Chinese medicine. Artificial
Intelligence in Medicine. 42: 229-245.
17. N.
L. Zhang,
Y. Wang and T. Chen (2008). Discovery
of latent structures. Experience with the COIL Challenge 2000 data. Journal
of Systems Science and Complexity. 21: 1-12.
18. N.
L. Zhang,
S.H. Yuan, Y. Wang and T. Chen (2008).
Latent structure models and syndrome differentiation in traditional Chinese
medicine (II): Analysis of Kidney Deficiency Data. Journal of Beijing
University of Chinese Medicine,31(9):584-587.
19. S.H.
Yuan, N. L. Zhang, T. Chen, and Y. Wang (2008). Latent structure models
and syndrome differentiation in traditional Chinese medicine (III): Model-based
syndrome differentiation versus syndrome differentiation by experts. Journal
of Beijing University of Chinese Medicine, 31(10):659-663.
20. N.
L. Zhang,
X. Z. Zhou, B. Y. Liu. And L. Y. He (2007). Interpreting
results of variable clustering in TCM research. Chinese Journal of
Information on Traditional Chinese Medicine. No. 7.
21. N.
L. Zhang
and S.H. Yuan (2006).
Latent structure models and syndrome differentiation in traditional Chinese
medicine (I): The basic idea and tools for latent structure analysis. Journal
of Beijing University of Chinese Medicine, 29(6):365-369.
22. N.L.
Zhang
and H.P. Guo (2006). Introduction to Bayesian
Networks. Science Press, Beijing.
23. Weihong
Zhang and N. L. Zhang (2005). Restricted Value Iteration: Theory and Algorithms,
Journal of Artificial Intelligence Research. 23: 123-165.
24. T.
Chen, T. Kocka, and N. L. Zhang (2005).Effective Dimensions of
Partially Observed Polytrees. International Journal of
Approximate Reasoning. 38(3): 311-332.
25. T.
D. Nielsen, Nevin Lianwen Zhang: Special Issue on ECSQARU-2003: The Seventh
European Conference on Symbolic and Quantitative Approaches to Reasoning with
Uncertainty: Message from the Guest Editors. Int.
J. Approx. Reasoning 38(3): 215-216 (2005)
26. N.
L. Zhang (2004).
Hierarchical latent class
models for cluster analysis. Journal of Machine Learning Research,
5(6):697--723, 2004.
27. N.
L. Zhang,
T. D. Nielsen, and F. V. Jensen (2004). Latent variable discovery
in classification models. Artificial Intelligence in
Medicine, 30(3): 283-299.
28. N. L. Zhang and Tomas Kocka (2004). Effective dimensions of hierarchical latent class
models. Journal of Artificial Intelligence Research, 21: 1-17.
29. D.
Poole and N. L. Zhang (2003). Exploiting contextual independence in probablisitic
inference. Journal of Artificial Intelligence Research, 18:
263-313.
30. N.
L. Zhang
(2002), Inference in Bayesian networks: The role of context-specific
independence, International Journal of Information Technology and Decision
Making (IJITDM), 1(1): 91-119.
31. N.
L. Zhang
and Zhang, W. (2001) "Speeding up the Convergence of
Value Iteration in Partially Observable Markov Decision Processes",
Journal of Artificial Intelligence Research, 14: 29-51.
32. N.
L. Zhang
(1998), Probabilistic Inference in Influence Diagrams, Computational Intelligence ,
14(4): 475-497. A shorter version appeared
in UAI-98.
33. N.
L. Zhang
(1998), Computational properties of two exact algorithms for Bayesian networks,
Applied Intelligence, 9(2): 173-183.
34. N.
L. Zhang and
W. Liu (1997), A model approximation scheme
for planning in partially observable stochastic domains, Journal of Artificial
Intelligence Research, 7: 199-230.
35. N.
L. Zhang and
Li Yan (1997), Independence of Causal Influence and Clique Tree
Propagation, International Journal of Approximate Reasoning, 19:
335-349.
36. N.
L. Zhang
and D. Poole (1996), Exploiting causal independence
in Bayesian network inference,Journal of Artificial Intelligence
Research, 5: 301-328.
37. N.
L. Zhang
(1996a), Irrelevance and parameter learning in Bayesian networks,
Artificial Intelligence, An International Journal, 88: 359-373.
38. N.
L. Zhang
R. Qi and D. Poole (1994a) A computational theory of decision networks, International
Journal of Approxi mate Reasoning, 1994, 11 (2): 83-158. Soft copy of the
paper is not available but click here for a
postscript file that is identical to the paper except for the format and page
numbers.
39. N.
L. Zhang
(1993). "Structural and functional quantization of vagueness", Fuzzy
Sets and Systems, 55: 51-60, 1993.
40. N.
L. Zhang (1993).
"Studies on hypergraphs I: hyperforests", Discrete Applied
Mathematics, 42: 95-112, 1993.
41. L.
Zhang
(1986), Weights of evidence and internal conflict for support functions,
Information Science, 38: 205-212.
Conference Articles
13. Tao
Chen, N. L. Zhang, and Yi Wang (2008). Efficient Model
Evaluation in the Search-Based Approach to Latent Structure Discovery. In
Proceedings of the Fourth European Workshop on Probabilistic Graphical
Models (PGM-08), 57-64.
14. Y.
Wang, N. L. Zhang. T. Chen (2008). Latent Tree
Models and Approximate Inference in Bayesian Networks, AAAI-08.
15. N.
L. Zhang,
S. H. Yuan, T. Chen, and Y. Wang (2007). Hierarchical Latent Class
Models and Statistical Foundation for Traditional Chinese Medicine
(long version), 11th Conference on
Artificial Intelligence in Medicine (AIME 07), 07-11, July 2007,
Amsterdam, The Netherlands.
16. N.
L. Zhang
(2007). Discovery of Latent Structures: Experience with the
CoIL Challenge 2000 Data Set. International Conference on Computational
Science (ICCS 2007). May 27-30, 2007.
17. T.
Chen and N. L. Zhang (2006). Quartet-based learning of shallow latent
variables. In Proceedings of the Third European Workshop on Probabilistic
Graphical Model,59-66 , September 12-15, 2006.
18. Y.
Wang and N. L. Zhang (2006). Severity of local maxima for the EM
algorithm: experiences with hierarchical latent class models. In Proceedings
of the Third European Workshop on Probabilistic Graphical Model, 301-308,
September 12-15, 2006.
19. T.
Chen and N. L. Zhang (2006). Quartet-Based Learning of
Hierarchical Latent Class Models: Discovery of Shallow Latent Variables.9th
International Symposium on Artificial Intelligence and Mathematics. Fort Lauderdale, Florida, January 4-6, 2006.
20. N. L. Zhang and T. Kocka (2004). Efficient Learning of Hierarchical Latent Class Models. Proc.
of the 16th IEEE International Conference on Tools with Artificial Intelligence
(ICTAI-2004), Boca Raton, Florida, November 15-17.
21. T. Kocha and N. L. Zhang (2003). Effective dimensions of partially observed polytrees. Proc.
of the 7th European Conference on Symbolic and Quantitave Approaches to
Reasoning with Uncertainty (ECSQARU-03), 184-195.
22. N.
L. Zhang
(2002). Hierarchical latent class models for cluster analysis. AAAI-02,
230-237.
23. T.
Kocka and N. L. Zhang (2002). Dimension correction for
hierarchical latent class models. Proc. of the 18th Conference on
Uncertainty in Artificial Intelligenc (UAI-02), 267-274.
24. N.
L. Zhang
and W. Zhang (2001b). Space-progressive value iteration: An anytime
algorithm for a class of POMDPs. Proc. of the 6th European
Conference on Symbolic and Quantitative Approaches to Reasoning with
Uncertainty (ECSQARU-01), 72-83.
25. S.P.M.
Choi, D.Y. Yeung, N.L. Zhang (1999) . An environment model for
nonstationary reinforcement learning. Advances in Neural
Information Processing Systems 12 (NIPS*99), pp.987-993, 1999.
26. N.
L. Zhang and
D. Poole (1999), On the role of
context-specific independence in Probabilistic Reasoning IJCAI-99,
1288-1293.
27. N.
L. Zhang ,
S. S. Lee, and W. Zhang (1999), A Method for Speeding Up Value
Iteration in Partially Observable Markov Decision Processes in Proc.
of the 15th Conference on Uncertainties in Artificial Intelligence, 696-703..
28. S. P. M. Choi , D. Y. Yeung, and N. L. Zhang
(1999), Hidden-mode Markov decision processes, Reinforcement
Learning , IJCAI'99 Workshop on neural, symbolic, and
reinforcement methods for sequence learning, 9-14.
29. Y.
Shen, D.L. Lee, N. L. Zhang (1999), A Distributed Search System
Based on Markov Decision Process , ICSC'99, December 1999, Hong
Kong.
30. N.
L. Zhang and
Stephen S. Lee (1998), Planing with Partially
Observable Markov Decision Processes: Advances in Exact Solution Methods in
Proc. of the 14th Conference on Uncertainties in Artificial Intelligence.
31. N.
L. Zhang
(1998), Context-specific independence, decomposition of conditional
probabilities, and inference in Bayesian networks, Proc of the Fifth Pacific
Rim International Conference on Artificial Intelligence, November 22-27,
Singapore, pp. 411-423.
32. N.
L. Zhang
(1998), Probabilistic inference in influence digrams, Proc. of the
Fourteenth Conference on Uncertainty in Artificial Intelligence,
pp.514-522.
33. N.
L. Zhang and
W. Liu (1997), Region-Based Approximations
for Planning in Stochastic Domains in Proc. of the 13th
Conference on Uncertainties in Artificial Intelligence.
34. N.
L. Zhang and
W. Zhang (1997), Fast Value Iteration for
Goal-Directed Markov Decision Processes in Proc. of the 13th
Conference on Uncertainties in Artificial Intelligence.
35. N.
L. Zhang
and L. Yan (1997), Independence of causal influence and clique tree
propagation, {\em Proc. of the Thirteenth Conference on Uncertainty in
Artificial Intelligence}, pp. 481-488.
36. Anthony
Cassandra, Michael L. Littman, and N. L. Zhang (1997), Incremental Pruning: A Simple, Fast, Exact Algorithm for Partially
Observable Markov Decision Processes in Proc. of the 13th
Conference on Uncertainties in Artificial Intelligence.
37. N.
L. Zhang
(1995), Inference with causal independence
in the CPSC network, in Proc. of the 11th Conference on
Uncertainties in Artificial Intelligence, Montreal, Canada, August.
38. N.
L. Zhang
and D. Poole (1994a), Intercausal independence and
heterogeneous factorization,i in Proc. of the 10th Conference on
Uncertainties in Artificial Intelligence., Seattle, USA, July 29-31.
39. N.
L. Zhang
and D. Poole (1994b), A simple approach to Bayesian
network computations, in Proc. of the 10th Canadian Conference on
Artificial Intelligence, Banff, Alberta, Canada, May 16-22.
40. N.
L. Zhang,
R. Qi, and D. Poole (1994b), Minimizing decision tables in influence
diagrams, in Selecting Models from Data : Artificial Intelligence
and Statistics IV, P. Cheeseman, R.W. Oldford, eds., Springer-Verlag.
41. N.
L. Zhang,
R. Qi, and D. Poole (1993), Incremental computation of the value of perfect
information in stepwise-decomposable influence diagrams, in Proc.
of the 9th International Conference on Uncertainty in Artificial Intelligence.
42. N.
L. Zhang,
R. Qi, and D. Poole (1992), Stepwise-decomposable influence diagrams,
in Proc. of the 3rd Conference on Knowledge representation, Cambridge,
Mass. USA, October 26-29.
Book/Book Chapters
1. T.
D. Nielsen and N. L. Zhang (eds.) (2003). Symbolic and Quantitative
Approaches to Reasoning with Uncertainty: Proceedings of the 7th European
Conference. Springer.
2. S.P.M.
Choi, D.Y. Yeung, N.L. Zhang (2000), Hidden-mode Markov decision
processes for nonstationary sequential decision making, in Sequence
Learning: Paradigms, Algorithms, and Application , R. Sun and G. Lee
(eds.), Springer-Verlag, Berlin. 264-287.
3. L.
Zhang (1994),
Interpretations of belief functions, in Advances in the Dempster-Shafer
Theory of Evidence, M. Fedrizzi et al (eds). John Wiley \& Sons,
Inc., New York, pp. 51-95.
4. N.
L. Zhang,
R. Qi, and D. Poole (1994b), Minimizing decision tables in influence diagrams,
in Selecting Models from Data : Artificial Intelligence and Statistics IV ,
P. Cheeseman and R.W. Oldford (eds.), Springer-Verlag.
Thesis
N. L. Zhang (1994). A computational theory of decision networks. Ph.D. Dissertation. Department of Computer Science, University of British Columbia.