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2020 – today
- 2021
[i30]Dan Geiger, David Heckerman:
Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions. CoRR abs/2105.03248 (2021)- [i29]David Heckerman, Dan Geiger:
Likelihoods and Parameter Priors for Bayesian Networks. CoRR abs/2105.06241 (2021)
2010 – 2019
- 2016
- [i28]Dan Geiger, David Heckerman:
Dependence and Relevance: A probabilistic view. CoRR abs/1611.02126 (2016) - 2014
- [i27]Ann Becker, Reuven Bar-Yehuda, Dan Geiger:
Random Algorithms for the Loop Cutset Problem. CoRR abs/1408.1483 (2014) - 2013
- [j30]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. Bioinform. 29(2): 197-205 (2013) - [j29]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. Bioinform. 29(5): 669 (2013) - [i26]Dan Geiger, Christopher Meek, Bernd Sturmfels:
Factorization of Discrete Probability Distributions. CoRR abs/1301.0568 (2013) - [i25]Dmitry Rusakov, Dan Geiger:
Asymptotic Model Selection for Naive Bayesian Networks. CoRR abs/1301.0598 (2013) - [i24]Ann Becker, Dan Geiger, Christopher Meek:
Perfect Tree-Like Markovian Distributions. CoRR abs/1301.3834 (2013) - [i23]Nir Friedman, Dan Geiger, Noam Lotner:
Likelihood Computations Using Value Abstractions. CoRR abs/1301.3855 (2013) - [i22]Dan Geiger, David Heckerman:
Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions. CoRR abs/1301.6697 (2013) - [i21]Dan Geiger, Christopher Meek:
Quantifier Elimination for Statistical Problems. CoRR abs/1301.6698 (2013) - [i20]Dan Geiger, Christopher Meek:
Graphical Models and Exponential Families. CoRR abs/1301.7376 (2013) - [i19]Ann Becker, Dan Geiger:
A Sufficiently Fast Algorithm for Finding Close to Optimal Junction Trees. CoRR abs/1302.3558 (2013) - [i18]Dan Geiger, David Heckerman, Christopher Meek:
Asymptotic Model Selection for Directed Networks with Hidden Variables. CoRR abs/1302.3580 (2013) - [i17]Dan Geiger, David Heckerman:
A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks. CoRR abs/1302.4949 (2013) - [i16]David Heckerman, Dan Geiger:
Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains. CoRR abs/1302.4957 (2013) - [i15]Ann Becker, Dan Geiger:
Approximation Algorithms for the Loop Cutset Problem. CoRR abs/1302.6787 (2013) - [i14]Dan Geiger, David Heckerman:
Learning Gaussian Networks. CoRR abs/1302.6808 (2013) - [i13]Dan Geiger, Azaria Paz, Judea Pearl:
On Testing Whether an Embedded Bayesian Network Represents a Probability Model. CoRR abs/1302.6809 (2013) - [i12]David Heckerman, Dan Geiger, David Maxwell Chickering:
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. CoRR abs/1302.6815 (2013) - [i11]Dan Geiger, David Heckerman:
Inference Algorithms for Similarity Networks. CoRR abs/1303.1493 (2013) - [i10]Dan Geiger:
An Entropy-based Learning Algorithm of Bayesian Conditional Trees. CoRR abs/1303.5403 (2013) - [i9]Dan Geiger, David Heckerman:
Advances in Probabilistic Reasoning. CoRR abs/1303.5718 (2013) - [i8]Dan Geiger, David Heckerman:
Practical and Theoretical Advances in Knowledge Acquisition of Probabilistic Networks. CoRR abs/1304.1145 (2013) - [i7]Dan Geiger, Tom S. Verma, Judea Pearl:
d-Separation: From Theorems to Algorithms. CoRR abs/1304.1505 (2013) - [i6]Dan Geiger, Judea Pearl:
On the Logic of Causal Models. CoRR abs/1304.2355 (2013) - [i5]Dan Geiger, Prakash P. Shenoy:
Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (1997). CoRR abs/1304.3846 (2013) - 2012
- [i4]Ydo Wexler, Dan Geiger:
Importance Sampling via Variational Optimization. CoRR abs/1206.5285 (2012) - [i3]Ari Frank, Dan Geiger, Zohar Yakhini:
A Distance-Based Branch and Bound Feature Selection Algorithm. CoRR abs/1212.2488 (2012) - [i2]Dmitry Rusakov, Dan Geiger:
Automated Analytic Asymptotic Evaluation of the Marginal Likelihood for Latent Models. CoRR abs/1212.2491 (2012) - 2011
- [j28]Barak Markus, Ohad S. Birk
, Dan Geiger:
Integration of SNP genotyping confidence scores in IBD inference. Bioinform. 27(20): 2880-2887 (2011) - [j27]Sivan Bercovici, Dan Geiger:
Admixture Aberration Analysis: Application to Mapping in Admixed Population Using Pooled DNA. J. Comput. Biol. 18(3): 237-249 (2011) - [j26]Dorit Baras, Shai Fine, Laurent Fournier, Dan Geiger, Avi Ziv:
Automatic boosting of cross-product coverage using Bayesian networks. Int. J. Softw. Tools Technol. Transf. 13(3): 247-261 (2011) - [i1]Reuven Bar-Yehuda, Ann Becker, Dan Geiger:
Randomized Algorithms for the Loop Cutset Problem. CoRR abs/1106.0225 (2011) - 2010
- [j25]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. Bioinform. 26(12): 175-182 (2010) - [c47]Sivan Bercovici, Dan Geiger:
Admixture Aberration Analysis: Application to Mapping in Admixed Population Using Pooled DNA. RECOMB 2010: 31-49
2000 – 2009
- 2009
- [j24]Dan Geiger, Christopher Meek, Ydo Wexler:
Speeding up HMM algorithms for genetic linkage analysis via chain reductions of the state space. Bioinform. 25(12) (2009) - [j23]Sivan Bercovici, Dan Geiger:
Inferring Ancestries Efficiently in Admixed Populations with Linkage Disequilibrium. J. Comput. Biol. 16(8): 1141-1150 (2009) - [c46]Mark Silberstein, Artyom Sharov, Dan Geiger, Assaf Schuster:
GridBot: execution of bags of tasks in multiple grids. SC 2009 - 2008
- [j22]Ydo Wexler, Dan Geiger:
Variational Upper and Lower Bounds for Probabilistic Graphical Models. J. Comput. Biol. 15(7): 721-735 (2008) - [c45]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 - [c44]Sivan Bercovici, Dan Geiger, Liran Shlush, Karl Skorecki, Alan A. Templeton:
Panel Construction for Mapping in Admixed Populations Via Expected Mutual Information. RECOMB 2008: 435-449 - 2007
- [j21]Ron Zohar, Dan Geiger:
Estimation of flows in flow networks. Eur. J. Oper. Res. 176(2): 691-706 (2007) - [c43]Ydo Wexler, Dan Geiger:
Variational Upper Bounds for Probabilistic Phylogenetic Models. RECOMB 2007: 226-237 - [c42]Ydo Wexler, Dan Geiger:
Importance Sampling via Variational Optimization. UAI 2007: 426-433 - 2006
- [j20]Dan Geiger, Christopher Meek, Ydo Wexler:
A Variational Inference Procedure Allowing Internal Structure for Overlapping Clusters and Deterministic Constraints. J. Artif. Intell. Res. 27: 1-23 (2006) - [c41]Mark Silberstein, Dan Geiger, Assaf Schuster:
A Distributed System for Genetic Linkage Analysis. GCCB 2006: 110-123 - [c40]Mark Silberstein, Dan Geiger, Assaf Schuster, Miron Livny:
Scheduling Mixed Workloads in Multi-grids: The Grid Execution Hierarchy. HPDC 2006: 291-302 - 2005
- [j19]Ydo Wexler, Zohar Yakhini, Yechezkel Kashi, Dan Geiger:
Finding Approximate Tandem Repeats in Genomic Sequences. J. Comput. Biol. 12(7): 928-942 (2005) - [j18]Dmitry Rusakov, Dan Geiger:
Asymptotic Model Selection for Naive Bayesian Networks. J. Mach. Learn. Res. 6: 1-35 (2005) - [c39]Dan Geiger, Christopher Meek:
Structured Variational Inference Procedures and their Realizations. AISTATS 2005: 104-111 - 2004
- [j17]Maáyan Fishelson, Dan Geiger:
Optimizing Exact Genetic Linkage Computations. J. Comput. Biol. 11(2/3): 263-275 (2004) - [j16]Gideon Greenspan, Dan Geiger:
Model-Based Inference of Haplotype Block Variation. J. Comput. Biol. 11(2/3): 493-504 (2004) - [c38]Gideon Greenspan, Dan Geiger:
High density linkage disequilibrium mapping using models of haplotype block variation. ISMB/ECCB (Supplement of Bioinformatics) 2004: 137-144 - [c37]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 - [c36]Ydo Wexler, Zohar Yakhini, Yechezkel Kashi, Dan Geiger:
Finding approximate tandem repeats in genomic sequences. RECOMB 2004: 223-232 - 2003
- [c35]Maáyan Fishelson, Dan Geiger:
Optimizing exact genetic linkage computations. RECOMB 2003: 114-121 - [c34]Gideon Greenspan, Dan Geiger:
Model-based inference of haplotype block variation. RECOMB 2003: 131-137 - [c33]Ari Frank, Dan Geiger, Zohar Yakhini:
A Distance-Based Branch and Bound Feature Selection Algorithm. UAI 2003: 241-248 - [c32]Dmitry Rusakov, Dan Geiger:
Automated Analytic Asymptotic Evaluation of the Marginal Likelihood for Latent Models. UAI 2003: 501-508 - 2002
- [c31]Maáyan Fishelson, Dan Geiger:
Exact genetic linkage computations for general pedigrees. ISMB 2002: 189-198 - [c30]Dan Geiger, Christopher Meek, Bernd Sturmfels:
Factorization of Discrete Probability Distributions. UAI 2002: 162-169 - [c29]Dmitry Rusakov, Dan Geiger:
Asymptotic Model Selection for Naive Bayesian Networks. UAI 2002: 438-445 - 2001
- [j15]Ann Becker, Dan Geiger:
A sufficiently fast algorithm for finding close to optimal clique trees. Artif. Intell. 125(1-2): 3-17 (2001) - [c28]Dmitry Rusakov, Dan Geiger:
On Parameter Priors for Discrete DAG Models. AISTATS 2001: 259-264 - 2000
- [j14]Ann Becker, Reuven Bar-Yehuda, Dan Geiger:
Randomized Algorithms for the Loop Cutset Problem. J. Artif. Intell. Res. 12: 219-234 (2000) - [c27]Ann Becker, Dan Geiger, Christopher Meek:
Perfect Tree-like Markovian Distributions. UAI 2000: 19-23 - [c26]Nir Friedman, Dan Geiger, Noam Lotner:
Likelihood Computations Using Value Abstraction. UAI 2000: 192-200
1990 – 1999
- 1999
- [j13]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) - [c25]Dan Geiger, David Heckerman, Henry King, Christopher Meek:
On the geometry of DAG models with hidden variables. AISTATS 1999 - [c24]Kristin P. Bennett, Usama M. Fayyad, Dan Geiger:
Density-Based Indexing for Approximate Nearest-Neighbor Queries. KDD 1999: 233-243 - [c23]Ann Becker, Reuven Bar-Yehuda, Dan Geiger:
Random Algorithms for the Loop Cutset Problem. UAI 1999: 49-56 - [c22]Dan Geiger, David Heckerman:
Parameter Priors for Directed Acyclic Graphical Models and the Characteriration of Several Probability Distributions. UAI 1999: 216-225 - [c21]Dan Geiger, Christopher Meek:
Quantifier Elimination for Statistical Problems. UAI 1999: 226-235 - 1998
- [j12]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) - [j11]Dan Geiger, David Heckerman:
Probabilistic relevance relations. IEEE Trans. Syst. Man Cybern. Part A 28(1): 17-25 (1998) - [c20]Dan Geiger:
Graphical Models and Exponential Families. UAI 1998: 156-165 - [p1]Dan Geiger, David Heckerman, Christopher Meek:
Asymptotic Model Selection for Directed Networks with Hidden Variables. Learning in Graphical Models 1998: 461-477 - 1997
- [j10]Nir Friedman, Dan Geiger, Moisés Goldszmidt:
Bayesian Network Classifiers. Mach. Learn. 29(2-3): 131-163 (1997) - [c19]Kirill Shoikhet, Dan Geiger:
A Practical Algorithm for Finding Optimal Triangulations. AAAI/IAAI 1997: 185-190 - [e1]Dan Geiger, Prakash P. Shenoy:
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 [contents] - 1996
- [j9]Dan Geiger, David Heckerman:
Knowledge Representation and Inference in Similarity Networks and Bayesian Multinets. Artif. Intell. 82(1-2): 45-74 (1996) - [j8]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) - [c18]Ann Becker, Dan Geiger:
A sufficiently fast algorithm for finding close to optimal junction trees. UAI 1996: 81-89 - [c17]Dan Geiger, David Heckerman, Christopher Meek:
Asymptotic Model Selection for Directed Networks with Hidden Variables. UAI 1996: 283-290 - 1995
- [j7]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) - [j6]David Heckerman, Dan Geiger, David Maxwell Chickering:
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. Mach. Learn. 20(3): 197-243 (1995) - [j5]Amir Eliaz, Dan Geiger:
Word-level recognition of small sets of hand-written words. Pattern Recognit. Lett. 16(10): 999-1009 (1995) - [c16]Dan Geiger, David Heckerman:
A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks. UAI 1995: 196-207 - [c15]David Heckerman, Dan Geiger:
Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains. UAI 1995: 274-284 - 1994
- [c14]David Heckerman, Dan Geiger, David Maxwell Chickering:
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. KDD Workshop 1994: 85-96 - [c13]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 - [c12]Ann Becker, Dan Geiger:
Approximation Algorithms for the Loop Cutset Problem. UAI 1994: 60-68 - [c11]Dan Geiger, David Heckerman:
Learning Gaussian Networks. UAI 1994: 235-243 - [c10]Dan Geiger, Azaria Paz, Judea Pearl:
On Testing Whether an Embedded Bayesian Network Represents a Probability Model. UAI 1994: 244-252 - [c9]David Heckerman, Dan Geiger, David Maxwell Chickering:
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. UAI 1994: 293-301 - 1993
- [c8]Dan Geiger, David Heckerman:
Inference Algorithms for Similarity Networks. UAI 1993: 326-334 - 1992
- [c7]Dan Geiger:
An Entropy-based Learning Algorithm of Bayesian Conditional Trees. UAI 1992: 92-97 - 1991
- [j4]Dan Geiger, Azaria Paz, Judea Pearl:
Axioms and Algorithms for Inferences Involving Probabilistic Independence. Inf. Comput. 91(1): 128-141 (1991) - [c6]Dan Geiger, Jeffrey A. Barnett:
Optimal Satisficing Tree Searches. AAAI 1991: 441-445 - [c5]Dan Geiger, David Heckerman:
Advances in Probabilistic Reasoning. UAI 1991: 118-126 - 1990
- [j3]Dan Geiger, Judea Pearl:
Logical and algorithmic properties of independence and their application to Bayesian networks. Ann. Math. Artif. Intell. 2: 165-178 (1990) - [j2]Dan Geiger, Thomas Verma, Judea Pearl:
Identifying independence in bayesian networks. Networks 20(5): 507-534 (1990) - [c4]Dan Geiger, Azaria Paz, Judea Pearl:
Learning Causal Trees from Dependence Information. AAAI 1990: 770-776 - [c3]Dan Geiger, David Heckerman:
separable and transitive graphoids. UAI 1990: 65-76
1980 – 1989
- 1989
- [j1]Judea Pearl, Dan Geiger, Thomas Verma:
Conditional independence and its representations. Kybernetika 25(7): 33-44 (1989) - [c2]Dan Geiger, Thomas Verma, Judea Pearl:
d-Separation: From Theorems to Algorithms. UAI 1989: 139-148 - 1988
- [c1]Dan Geiger, Judea Pearl:
On the logic of causal models. UAI 1988: 3-14
Coauthor Index
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