Monday, December 7, 2015

huja, R. and J. Orlin (2001). A fast scaling al-
gorithm for minimizing separable convex func-
tions subject to chain constraints.
Research 49
(5), 784–789.
Altendorf, E., A. Restificar, and T. Dietterich
(2005). Learning from sparse data by exploit-
ing monotonicity constraints. In F. Bacchus and
T. Jaakkola (Eds.),
Proceedings of the 21st Con-
ference on Uncertainty in Artificial Intelligence
, pp. 18–25. AUAI Press.
Blake, C. and C. Merz (1998). UCI repos-
itory of machine learning databases
Brunk, H. (1965). Conditional expectation given a
-lattice and applications.
Annals of Mathemat-
ical Statistics 36
, 1339–1350.
Dykstra, R. and C. Feltz (1989). Nonparametric
maximum likelihood estimation of survival func-
tions with a general stochastic ordering and its
Biometrika 76
(2), 331–341.
Dykstra, R. and T. Robertson (1982). An algorithm
for isotonic regression for two or more indepen-
dent variables.
The Annals of Statistics 10
El Barmi, H. and H. Mukerjee (2005). Inferences
under a stochastic ordering constraint: the k-
sample case.
Journal of the American Statistical
Association 100
(469), 252–261.
Fayyad, U. and K. Irani (1993). Multi-interval dis-
cretization of continuous valued attributes for
classification learning. In
Proceedings of IJCAI-
93 (volume 2)
, pp. 1022–1027. Morgan Kauf-
Feelders, A. and L. van der Gaag (2005). Learn-
ing Bayesian network parameters with prior
knowledge about context-specific qualitative in-
fluences. In F. Bacchus and T. Jaakkola (Eds.),
Proceedings of the 21st Conference on Uncer-
tainty in Artificial Intelligence (UAI-05)
, pp.
193–200. AUAI Press.
Hoff, P. (2003). Nonparametric estimation of convex
models via mixtures.
Annals of Statistics 31
Hogg, R. (1965). On models and hypotheses with
restricted alternatives.
Journal of the American
Statistical Association 60
(312), 1153–1162.
Koop, G. (2000).
Analysis of Economic Data
. John
Wiley and Sons.
Maxwell, W. and J. Muchstadt (1985). Establish-
ing consistent and realistic reorder intervals in
production-distribution systems.
Operations Re-
search 33
, 1316–1341.
McLachlan, G. and T. Krishnan (1997).
The EM
algorithm and extensions
. Wiley.
Niculescu, R., T. Mitchell, and R. Rao (2006).
Bayesian network learning with parameter con-
Journal of machine learning research 7
Niculescu, R., T. Mitchell, and R. Rao (2007). A
theoretical framework for learning Bayesian net-
works with parameter inequality constraints. In
Proceedings of the twentieth international joint
conference on artificial intelligence
, pp. 155–160.
Robertson, T., F. Wright, and R. Dykstra (1988).
Order Restricted Statistical Inference
. Wiley.
Wellman, M. (1990). Fundamental concepts of qual-
itative probabilistic networks.
Artificial Intelli-
gence 44
, 257–303.
Wittig, F. and A. Jameson (2000). Exploiting
qualitative knowledge in the learning of con-
ditional probabilities of Bayesian networks. In
C. Boutilier and M. Goldszmidt (Eds.),
ings of the Sixteenth Conference on Uncertainty
in Artificial Intelligence
, pp. 644–652. Morgan

No comments:

Post a Comment