[1] Dan Geiger and Judea Pearl. On the logic of causal models. In UAI, pages 3–14, 1988. [ bib | http | .pdf ]
[2] Dan Geiger and Judea Pearl. Logical and algorithmic properties of conditional independence. In AISTATS, 1989. [ bib ]
[3] Dan Geiger, Thomas Verma, and Judea Pearl. d-separation: From theorems to algorithms. In UAI, pages 139–148, 1989. [ bib | http | .pdf ]
[4] Dan Geiger and David Heckerman. separable and transitive graphoids. In UAI, pages 65–76, 1990. [ bib | http | .pdf ]
[5] Dan Geiger, Azaria Paz, and Judea Pearl. Learning causal trees from dependence information. In AAAI, pages 770–776, 1990. [ bib | http | .pdf ]
[6] Dan Geiger and Jeffrey A. Barnett. Optimal satisficing tree searches. In AAAI, pages 441–445, 1991. [ bib | http | .pdf ]
[7] Dan Geiger and David Heckerman. Advances in probabilistic reasoning. In UAI, pages 118–126, 1991. [ bib | http | .pdf ]
[8] Dan Geiger. An entropy-based learning algorithm of Bayesian conditional trees. In UAI, pages 92–97, 1992. [ bib | http | .pdf ]
[9] Dan Geiger and David Heckerman. Inference algorithms for similarity networks. In UAI, pages 326–334, 1993. [ bib | http | .pdf ]
[10] Dan Geiger, Azaria Paz, and Judea Pearl. On testing whether an embedded Bayesian network represents a probability model. In UAI, pages 244–252, 1994. [ bib | http | .pdf ]
[11] David Heckerman, Dan Geiger, and David M. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. In UAI, pages 293–301, 1994. [ bib | http | .pdf ]
[12] Ann Becker and Dan Geiger. Approximation algorithms for the loop cutset problem. In UAI, pages 60–68, 1994. [ bib | http | .pdf ]
[13] Amir Eliaz and Dan Geiger. Word-level recognition of small sets of hand written words. In SSPR, 1994. [ bib | .pdf ]
[14] Dan Geiger and David Heckerman. Learning Gaussian networks. In UAI, pages 235–243, 1994. [ bib | http | .pdf ]
[15] Reuven Bar-Yehuda, Dan Geiger, Joseph Naor, and Ron M. Roth. Approximation algorithms for the vertex feedback set problem with applications to constraint satisfaction and Bayesian inference. In SODA, pages 344–354, 1994. [ bib | http | .pdf ]
[16] David Heckerman and Dan Geiger. Learning Bayesian networks: A unification for discrete and Gaussian domains. In UAI, pages 274–284, 1995. [ bib |http | .pdf ]
[17] David Maxwell Chickering, Dan Geiger, and David Heckerman. Learning Bayesian networks: Search methods and experimental results. In AISTATS, pages 112–128, 1995. [ bib | .pdf ]
[18] Dan Geiger and David Heckerman. A characterization of the Dirichlet distribution with application to learning Bayesian networks. In UAI, pages 196–207, 1995. [ bib | http | .pdf ]
[19] Dan Geiger, David Heckerman, and Christopher Meek. Asymptotic model selection for directed networks with hidden variables. In UAI, pages 283–290, 1996. [ bib | http | .pdf ]
[20] Ann Becker and Dan Geiger. A sufficiently fast algorithm for finding close to optimal junction trees. In UAI, pages 81–89, 1996. [ bib | http | .pdf ]
[21] Kirill Shoikhet and Dan Geiger. A practical algorithm for finding optimal triangulations. In AAAI/IAAI, pages 185–190, 1997. [ bib | http | .pdf ]
22] Dan Geiger. Graphical models and exponential families. In UAI, pages 156–165, 1998. [ bib | http | .pdf ]
[23] Dan Geiger, David Heckerman, Henry King, and Christopher Meek. On the geometry of DAG models with hidden variables. In AISTATS, 1999. [ bib ]
[24] Kristin P. Bennett, Usama M. Fayyad, and Dan Geiger. Density-based indexing for approximate nearest-neighbor queries. In KDD, pages 233–243, 1999. [ bib | http | .pdf ]
[25] Dan Geiger and Christopher Meek. Quantifier elimination for statistical problems. In UAI, pages 226–235, 1999. [ bib | http | .pdf ]
[26] Ann Becker, Reuven Bar-Yehuda, and Dan Geiger. Random algorithms for the loop cutset problem. In UAI, pages 49–56, 1999. [ bib | http | .pdf ]
[27] Dan Geiger and David Heckerman. Parameter priors for directed acyclic graphical models and the characteriration of several probability distributions. InUAI, pages 216–225, 1999. [ bib | http | .pdf ]
[28] Ann Becker, Dan Geiger, and Christopher Meek. Perfect tree-like Markovian distributions. In UAI, pages 19–23, 2000. [ bib | http | .pdf ]
[29] Nir Friedman, Dan Geiger, and Noam Lotner. Likelihood computations using value abstraction. In UAI, pages 192–200, 2000. [ bib | http | .pdf ]
[30] Dmitry Rusakov and Dan Geiger. On parameter priors for discrete DAG models. In AISTATS, 2001. [ bib | http | .pdf ]
[31] Maáyan Fishelson and Dan Geiger. Exact genetic linkage computations for general pedigrees. In ISMB, pages 189–198, 2002. [ bib | .pdf ]
[32] Dan Geiger, Christopher Meek, and Bernd Sturmfels. Factorization of discrete probability distributions. In UAI, pages 162–169, 2002. [ bib | http | .pdf ]
[33] Dmitry Rusakov and Dan Geiger. Asymptotic model selection for naive Bayesian networks. In UAI, pages 438–445, 2002. [ bib | http | .pdf ]
[34] Maáyan Fishelson and Dan Geiger. Optimizing exact genetic linkage computations. In RECOMB, pages 114–121, 2003. [ bib | http | .pdf ]
[35] Gideon Greenspan and Dan Geiger. Model-based inference of haplotype block variation. In RECOMB, pages 131–137, 2003. [ bib | http | .pdf ]
[36] Ari Frank, Dan Geiger, and Zohar Yakhini. A distance-based branch and bound feature selection algorithm. In UAI, pages 241–248, 2003. [ bib | http | .pdf ]
[37] Dmitry Rusakov and Dan Geiger. Automated analytic asymptotic evaluation of the marginal likelihood for latent models. In UAI, pages 501–508, 2003. [ bib | http | .pdf ]
[38] Ydo Wexler, Zohar Yakhini, Yechezkel Kashi, and Dan Geiger. Finding approximate tandem repeats in genomic sequences. In RECOMB, pages 223–232, 2004. [ bib | http | .pdf ]
[39] Gideon Greenspan and Dan Geiger. High density linkage disequilibrium mapping using models of haplotype block variation. In ISMB/ECCB (Supplement of Bioinformatics), pages 137–144, 2004. [ bib | http | .pdf ]
[40] Vladimir Jojic, Nebojsa Jojic, Christopher Meek, Dan Geiger, Adam C. Siepel, David Haussler, and David Heckerman. Efficient approximations for learning phylogenetic HMM models from data. In ISMB/ECCB (Supplement of Bioinformatics), pages 161–168, 2004. [ bib | http | .pdf ]
[41] Dan Geiger and Christopher Meek. Structured variational inference procedures and their realizations. In AISTATS, 2005. [ bib | http | .pdf ]
[42] Mark Silberstein, Dan Geiger, Assaf Schuster, and Miron Livny. Scheduling mixed workloads in multi-grids: The grid execution hierarchy. In HPDC, pages 291–302, 2006. [ bib | http | .pdf ]
[43] Mark Silberstein, Dan Geiger, and Assaf Schuster. A distributed system for genetic linkage analysis. In GCCB, pages 110–123, 2006. [ bib | http | .pdf ]
[44] Ydo Wexler and Dan Geiger. Variational upper bounds for probabilistic phylogenetic models. In RECOMB, pages 226–237, 2007. [ bib | http | .pdf ]
[45] Ydo Wexler and Dan Geiger. Importance sampling via variational optimization. In UAI, pages 426–433, 2007. [ bib | http | .pdf ]
[46] Mark Silberstein, Assaf Schuster, Dan Geiger, Anjul Patney, and John D. Owens. Efficient computation of sum-products on GPUs through software-managed cache. In ICS, pages 309–318, 2008. [ bib | http | .pdf ]
[47] Sivan Bercovici, Dan Geiger, Liran Shlush, Karl Skorecki, and Alan Templeton. Panel construction for mapping in admixed populations via expected mutual information. In RECOMB, pages 435–449, 2008. [ bib | http | .pdf ]
[48] Mark Silberstein, Artyom Sharov, Dan Geiger, and Assaf Schuster. Gridbot: execution of bags of tasks in multiple grids. In SC, 2009. [ bib | http | .pdf ]
[49] Sivan Bercovici and Dan Geiger. Admixture aberration analysis: Application to mapping in admixed population using pooled DNA. In RECOMB, pages 31–49, 2010. [ bib | http | .pdf ]
[50] Sarig O., Bercovici S., Zoller L., Indelman M., Goldberg I., Bergman R., Israeli S., Sagiv N., Rosenberg S., Darvasi A., Geiger D., and Sprecher E. Pemphigus Vulgaris: A genome-wide association study. In SID, 2010. [ bib ]
[51] Sivan Bercovici, Christopher Meek, Ydo Wexler, and Dan Geiger. Estimating genome-wide IBD sharing from SNP data via an efficient hidden Markov model of LD with application to gene mapping. Bioinformatics [ISMB], 26(12):175–182, 2010. [ bib | http | .pdf ]