Dan Geiger

Mark Silberstein, Omer Weissbrod, Lars Otten, Anna Tzemach, Andrei Anisenia, Oren Shtark, Dvir Tuberg, Eddie Galfrin, Irena Gannon, Adel Shalata, Zvi U. Borochowitz, Rina Dechter, Elizabeth Thompson, Dan Geiger: A system for exact and approximate genetic linkage analysis of SNP data in large pedigrees. Bioinformatics 29(2): 197-205 (2013)

Mark Silberstein, Omer Weissbrod, Lars Otten, Anna Tzemach, Andrei Anisenia, Oren Shtark, Dvir Tuberg, Eddie Galfrin, Irena Gannon, Adel Shalata, Zvi U. Borochowitz, Rina Dechter, Elizabeth Thompson, Dan Geiger: A system for exact and approximate genetic linkage analysis of SNP data in large pedigrees. Bioinformatics 29(5): 669 (2013)

Dan Geiger, Christopher Meek, Bernd Sturmfels: Factorization of Discrete Probability Distributions. CoRR abs/1301.0568 (2013)

Dmitry Rusakov, Dan Geiger: Asymptotic Model Selection for Naive Bayesian Networks. CoRR abs/1301.0598 (2013)

Ann Becker, Dan Geiger, Christopher Meek: Perfect Tree-Like Markovian Distributions. CoRR abs/1301.3834 (2013)

Nir Friedman, Dan Geiger, Noam Lotner: Likelihood Computations Using Value Abstractions. CoRR abs/1301.3855 (2013)

Dan Geiger, David Heckerman: Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions. CoRR abs/1301.6697 (2013)

Dan Geiger, Christopher Meek: Quantifier Elimination for Statistical Problems. CoRR abs/1301.6698 (2013)

Dan Geiger, Christopher Meek: Graphical Models and Exponential Families. CoRR abs/1301.7376 (2013)

Ann Becker, Dan Geiger: A Sufficiently Fast Algorithm for Finding Close to Optimal Junction Trees. CoRR abs/1302.3558 (2013)

Dan Geiger, David Heckerman, Christopher Meek: Asymptotic Model Selection for Directed Networks with Hidden Variables. CoRR abs/1302.3580 (2013)

Dan Geiger, David Heckerman: A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks. CoRR abs/1302.4949 (2013)

David Heckerman, Dan Geiger: Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains. CoRR abs/1302.4957 (2013)

Ann Becker, Dan Geiger: Approximation Algorithms for the Loop Cutset Problem. CoRR abs/1302.6787 (2013)

Dan Geiger, David Heckerman: Learning Gaussian Networks. CoRR abs/1302.6808 (2013)

Dan Geiger, Azaria Paz, Judea Pearl: On Testing Whether an Embedded Bayesian Network Represents a Probability Model. CoRR abs/1302.6809 (2013)

David Heckerman, Dan Geiger, David Maxwell Chickering: Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. CoRR abs/1302.6815 (2013)

Dan Geiger, David Heckerman: Inference Algorithms for Similarity Networks. CoRR abs/1303.1493 (2013)

Dan Geiger: An Entropy-based Learning Algorithm of Bayesian Conditional Trees. CoRR abs/1303.5403 (2013)

Dan Geiger, David Heckerman: Advances in Probabilistic Reasoning. CoRR abs/1303.5718 (2013)

Dan Geiger, David Heckerman: Practical and Theoretical Advances in Knowledge Acquisition of Probabilistic Networks. CoRR abs/1304.1145 (2013)

Dan Geiger, Tom S. Verma, Judea Pearl: d-Separation: From Theorems to Algorithms. CoRR abs/1304.1505 (2013)

Dan Geiger, Judea Pearl: On the Logic of Causal Models. CoRR abs/1304.2355 (2013)

Dan Geiger, Prakash P. Shenoy: Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (1997). CoRR abs/1304.3846 (2013)

Ydo Wexler, Dan Geiger: Importance Sampling via Variational Optimization. CoRR abs/1206.5285 (2012)

Ari Frank, Dan Geiger, Zohar Yakhini: A Distance-Based Branch and Bound Feature Selection Algorithm. CoRR abs/1212.2488 (2012)

Dmitry Rusakov, Dan Geiger: Automated Analytic Asymptotic Evaluation of the Marginal Likelihood for Latent Models. CoRR abs/1212.2491 (2012)

Barak Markus, Ohad S. Birk, Dan Geiger: Integration of SNP genotyping confidence scores in IBD inference. Bioinformatics 27(20): 2880-2887 (2011)

Sivan Bercovici, Dan Geiger: Admixture Aberration Analysis: Application to Mapping in Admixed Population Using Pooled DNA. Journal of Computational Biology 18(3): 237-249 (2011)

Dorit Baras, Shai Fine, Laurent Fournier, Dan Geiger, Avi Ziv: Automatic boosting of cross-product coverage using Bayesian networks. STTT 13(3): 247-261 (2011)

Reuven Bar-Yehuda, Ann Becker, Dan Geiger: Randomized Algorithms for the Loop Cutset Problem. CoRR abs/1106.0225 (2011)

Sivan Bercovici, Christopher Meek, Ydo Wexler, 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)

Sivan Bercovici, Dan Geiger: Admixture Aberration Analysis: Application to Mapping in Admixed Population Using Pooled DNA. RECOMB 2010: 31-49

Dan Geiger, Christopher Meek, Ydo Wexler: Speeding up HMM algorithms for genetic linkage analysis via chain reductions of the state space. Bioinformatics 25(12) (2009)

Sivan Bercovici, Dan Geiger: Inferring Ancestries Efficiently in Admixed Populations with Linkage Disequilibrium. Journal of Computational Biology 16(8): 1141-1150 (2009)

Mark Silberstein, Artyom Sharov, Dan Geiger, Assaf Schuster: GridBot: execution of bags of tasks in multiple grids. SC 2009

Ydo Wexler, Dan Geiger: Variational Upper and Lower Bounds for Probabilistic Graphical Models. Journal of Computational Biology 15(7): 721-735 (2008)

Mark Silberstein, Assaf Schuster, Dan Geiger, Anjul Patney, John D. Owens: Efficient computation of sum-products on GPUs through software-managed cache. ICS 2008: 309-318

Sivan Bercovici, Dan Geiger, Liran Shlush, Karl Skorecki, Alan Templeton: Panel Construction for Mapping in Admixed Populations Via Expected Mutual Information. RECOMB 2008: 435-449

Ron Zohar, Dan Geiger: Estimation of flows in flow networks. European Journal of Operational Research 176(2): 691-706 (2007)

Ydo Wexler, Dan Geiger: Variational Upper Bounds for Probabilistic Phylogenetic Models. RECOMB 2007: 226-237

Ydo Wexler, Dan Geiger: Importance Sampling via Variational Optimization. UAI 2007: 426-433

Dan Geiger, Christopher Meek, Ydo Wexler: A Variational Inference Procedure Allowing Internal Structure for Overlapping Clusters and Deterministic Constraints. J. Artif. Intell. Res. (JAIR) 27: 1-23 (2006)

Mark Silberstein, Dan Geiger, Assaf Schuster: A Distributed System for Genetic Linkage Analysis. GCCB 2006: 110-123

Mark Silberstein, Dan Geiger, Assaf Schuster, Miron Livny: Scheduling Mixed Workloads in Multi-grids: The Grid Execution Hierarchy. HPDC 2006: 291-302

Ydo Wexler, Zohar Yakhini, Yechezkel Kashi, Dan Geiger: Finding Approximate Tandem Repeats in Genomic Sequences. Journal of Computational Biology 12(7): 928-942 (2005)

Dmitry Rusakov, Dan Geiger: Asymptotic Model Selection for Naive Bayesian Networks. Journal of Machine Learning Research 6: 1-35 (2005)

Maáyan Fishelson, Dan Geiger: Optimizing Exact Genetic Linkage Computations. Journal of Computational Biology 11(2/3): 263-275 (2004)

Gideon Greenspan, Dan Geiger: Model-Based Inference of Haplotype Block Variation. Journal of Computational Biology 11(2/3): 493-504 (2004)

Gideon Greenspan, Dan Geiger: High density linkage disequilibrium mapping using models of haplotype block variation. ISMB/ECCB (Supplement of Bioinformatics) 2004: 137-144

Vladimir Jojic, Nebojsa Jojic, Christopher Meek, Dan Geiger, Adam C. Siepel, David Haussler, David Heckerman: Efficient approximations for learning phylogenetic HMM models from data. ISMB/ECCB (Supplement of Bioinformatics) 2004: 161-168

Ydo Wexler, Zohar Yakhini, Yechezkel Kashi, Dan Geiger: Finding approximate tandem repeats in genomic sequences. RECOMB 2004: 223-232

Maáyan Fishelson, Dan Geiger: Optimizing exact genetic linkage computations. RECOMB 2003: 114-121

Gideon Greenspan, Dan Geiger: Model-based inference of haplotype block variation. RECOMB 2003: 131-137

Ari Frank, Dan Geiger, Zohar Yakhini: A Distance-Based Branch and Bound Feature Selection Algorithm. UAI 2003: 241-248

Dmitry Rusakov, Dan Geiger: Automated Analytic Asymptotic Evaluation of the Marginal Likelihood for Latent Models. UAI 2003: 501-508

Maáyan Fishelson, Dan Geiger: Exact genetic linkage computations for general pedigrees. ISMB 2002: 189-198

Dan Geiger, Christopher Meek, Bernd Sturmfels: Factorization of Discrete Probability Distributions. UAI 2002: 162-169

Dmitry Rusakov, Dan Geiger: Asymptotic Model Selection for Naive Bayesian Networks. UAI 2002: 438-445

Ann Becker, Dan Geiger: A sufficiently fast algorithm for finding close to optimal clique trees. Artif. Intell. 125(1-2): 3-17 (2001)

Ann Becker, Reuven Bar-Yehuda, Dan Geiger: Randomized Algorithms for the Loop Cutset Problem. J. Artif. Intell. Res. (JAIR) 12: 219-234 (2000)

Ann Becker, Dan Geiger, Christopher Meek: Perfect Tree-like Markovian Distributions. UAI 2000: 19-23

Nir Friedman, Dan Geiger, Noam Lotner: Likelihood Computations Using Value Abstraction. UAI 2000: 192-200

Laxmi Parida, Dan Geiger: Mass Estimation of DNA Molecules and Extraction of Ordered Restriction Maps in Optical Mapping Imagery. Algorithmica 25(2-3): 295-310 (1999)

Kristin P. Bennett, Usama M. Fayyad, Dan Geiger: Density-Based Indexing for Approximate Nearest-Neighbor Queries. KDD 1999: 233-243

Ann Becker, Reuven Bar-Yehuda, Dan Geiger: Random Algorithms for the Loop Cutset Problem. UAI 1999: 49-56

Dan Geiger, David Heckerman: Parameter Priors for Directed Acyclic Graphical Models and the Characteriration of Several Probability Distributions. UAI 1999: 216-225

Dan Geiger, Christopher Meek: Quantifier Elimination for Statistical Problems. UAI 1999: 226-235

Reuven Bar-Yehuda, Dan Geiger, Joseph Naor, Ron M. Roth: Approximation Algorithms for the Feedback Vertex Set Problem with Applications to Constraint Satisfaction and Bayesian Inference. SIAM J. Comput. 27(4): 942-959 (1998)

Dan Geiger, David Heckerman: Probabilistic relevance relations. IEEE Transactions on Systems, Man, and Cybernetics, Part A 28(1): 17-25 (1998)

Dan Geiger: Graphical Models and Exponential Families. UAI 1998: 156-165

Nir Friedman, Dan Geiger, Moisés Goldszmidt: Bayesian Network Classifiers. Machine Learning 29(2-3): 131-163 (1997)

Kirill Shoikhet, Dan Geiger: A Practical Algorithm for Finding Optimal Triangulations. AAAI/IAAI 1997: 185-190

Dan Geiger, Prakash P. Shenoy (Eds.): UAI '97: Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, Brown University, Providence, Rhode Island, USA, August 1-3, 1997. Morgan Kaufmann 1997, isbn 1-55860-485-5

Dan Geiger, David Heckerman: Knowledge Representation and Inference in Similarity Networks and Bayesian Multinets. Artif. Intell. 82(1-2): 45-74 (1996)

Ann Becker, Dan Geiger: Optimization of Pearl's Method of Conditioning and Greedy-Like Approximation Algorithms for the Vertex Feedback Set Problem. Artif. Intell. 83(1): 167-188 (1996)

Ann Becker, Dan Geiger: A sufficiently fast algorithm for finding close to optimal junction trees. UAI 1996: 81-89

Dan Geiger, David Heckerman, Christopher Meek: Asymptotic Model Selection for Directed Networks with Hidden Variables. UAI 1996: 283-290

David Maxwell Chickering, Dan Geiger, David Heckerman: On Finding a Cycle Basis with a Shortest Maximal Cycle. Inf. Process. Lett. 54(1): 55-58 (1995)

David Heckerman, Dan Geiger, David Maxwell Chickering: Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. Machine Learning 20(3): 197-243 (1995)

Amir Eliaz, Dan Geiger: Word-level recognition of small sets of hand-written words. Pattern Recognition Letters 16(10): 999-1009 (1995)

Dan Geiger, David Heckerman: A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks. UAI 1995: 196-207

David Heckerman, Dan Geiger: Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains. UAI 1995: 274-284

David Heckerman, Dan Geiger, David Maxwell Chickering: Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. KDD Workshop 1994: 85-96

Reuven Bar-Yehuda, Dan Geiger, Joseph Naor, Ron M. Roth: Approximation Algorithms for the Vertex Feedback Set Problem with Applications to Constraint Satisfaction and Bayesian Inference. SODA 1994: 344-354

Ann Becker, Dan Geiger: Approximation Algorithms for the Loop Cutset Problem. UAI 1994: 60-68

Dan Geiger, David Heckerman: Learning Gaussian Networks. UAI 1994: 235-243

Dan Geiger, Azaria Paz, Judea Pearl: On Testing Whether an Embedded Bayesian Network Represents a Probability Model. UAI 1994: 244-252

David Heckerman, Dan Geiger, David Maxwell Chickering: Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. UAI 1994: 293-301

Dan Geiger, David Heckerman: Inference Algorithms for Similarity Networks. UAI 1993: 326-334

Dan Geiger: An Entropy-based Learning Algorithm of Bayesian Conditional Trees. UAI 1992: 92-97

Dan Geiger, Azaria Paz, Judea Pearl: Axioms and Algorithms for Inferences Involving Probabilistic Independence. Inf. Comput. 91(1): 128-141 (1991)

Dan Geiger, Jeffrey A. Barnett: Optimal Satisficing Tree Searches. AAAI 1991: 441-445

Dan Geiger, David Heckerman: Advances in Probabilistic Reasoning. UAI 1991: 118-126

Dan Geiger, Judea Pearl: Logical and algorithmic properties of independence and their application to Bayesian networks. Ann. Math. Artif. Intell. 2: 165-178 (1990)

Dan Geiger, Thomas Verma, Judea Pearl: Identifying independence in bayesian networks. Networks 20(5): 507-534 (1990)

Dan Geiger, Azaria Paz, Judea Pearl: Learning Causal Trees from Dependence Information. AAAI 1990: 770-776

Dan Geiger, David Heckerman: separable and transitive graphoids. UAI 1990: 65-76

Dan Geiger, Thomas Verma, Judea Pearl: d-Separation: From Theorems to Algorithms. UAI 1989: 139-148

Dan Geiger, Judea Pearl: On the logic of causal models. UAI 1988: 3-14