David
Heckerman
Senior Director, eScience Research Group, Microsoft Research
E-mail: heckerma@microsoft.comMail: 1100 Glendon Ave Suite 1080, Los Angeles CA 90024
Research activities
I am interested in learning from data. The models and methods I use are inspired by work in the fields of statistics and data analysis, machine learning, probability theory, decision theory, decision analysis, and artificial intelligence. My recent work has concentrated on using graphical models for data analysis and visualization in biology and medicine with a special focus on the design of HIV vaccines.
Selected
publications
- D.
Heckerman. A
Tutorial on Learning with Bayesian Networks. In Learning in Graphical Models, M. Jordan, ed.. MIT Press,
· D. Heckerman. Probabilistic Similarity Networks. MIT Press, Cambridge, MA, 1991.
All publications
· C. Rousseau, D. Lockhart, J. Listgarten, S. Maley, C. Kadie, G. Learn, D. Nickle, D. Heckerman, W. Deng, C. Brander, T. Ndung'u, H. Coovadia, P. Goulder, B. Korber, B. Walker, J. Mullins. Rare HLA drive additional HIV evolution compared to more frequent alleles. AIDS Res Hum Retroviruses, 25:297-303, March 2009.
· Y. Kawashima, K. Pfafferott, J. Frater, P. Matthews, R. Payne, M. Addo, H. Gatanaga, M. Fujiwara, A. Hachiya, H. Koizumi, N. Kuse, S. Oka, A. Duda, A. Prendergast, H. Crawford, A. Leslie, Z. Brumme, C. Brumme, T. Allen, C. Brander, R. Kaslow, J. Tang, E. Hunter, S. Allen, J. Mulenga, S. Branch, T. Roach, M. John, S. Mallal, A. Ogwu, R. Shapiro, J. Prado, S. Fidler, J. Weber, O. Pybus, P. Klenerman, T. Ndung'u, R. Phillips, D. Heckerman, P. Harrigan, B. Walker, M. Takiguchi, and P. Goulder. Adaptation of HIV-1 to human leukocyte antigen class I. Nature, February 2009.
· T. Miura, M. Brockman, Z. Brumme, C. Brumme, F. Pereyra, A. Trocha, B. Block, A. Schneidewind, T. Allen, D. Heckerman, and B. Walker. HLA-associated alterations in replication capacity of chimeric NL4-3 viruses carrying gag-protease from elite controllers of human immunodeficiency virus type 1. Journal of Virology, 83:140-149. January 2009.
· J. Carlson, Z. Brumme, C. Rousseau, C. Brumme, P. Matthews, C. Kadie, J. Mullins, B. Walker, P. Harrigan, P. Goulder, D. Heckerman. Phylogenetic dependency networks: Inferring patterns of CTL escape and codon covariation in HIV-1 Gag. PLoS Computational Biology, 4(11): e1000225, November 2008.
- Y. Wang, B. Li, J. Carlson, H. Streeck, A. Gladden, R. Goodman, A. Schneidewind, K. Power, I. Toth, N. Frahm, G. Alter, C. Brander, M. Carrington, B. Walker, M. Altfeld, D. Heckerman, and T. Allen. Protective HLA class I alleles restricting acute-phase CD8+ T cell responses are associated with viral escape mutations located in highly conserved regions of HIV-1. Journal of Virology. November, 2008.
- D. Yerly, D. Heckerman, T. Allen, T. Suscovich, N. Jojic, C. Kadie, W. Pichler, A. Cerny, and C. Brander. Design, expression, and processing of epitomized hepatitis C virus-encoded CTL epitopes. Journal of Immunology, 181:6361-6370, November, 2008.
- P. Matthews, A. Prendergast, A. Leslie, H. Crawford, R. Payne, C. Rousseau, I. Honeyborne, J. Carlson, C. Kadie, C. Brander, J. Mullins, H. Coovadia, T. Nding.u, B. Walker, D. Heckerman, P. Goulder. Central role of reverting mutations in HLA associations with human immunodeficiency virus set point. Journal of Virology, 82:8548-59, September, 2008.
- Z. Brumme, C. Brumme, J. Carlson, H. Streeck, M. John, Q. Eichbaum, B. Block, B. Baker, C. Kadie, M. Markowitz, H. Jessen, A. Kelleher, E. Rosenberg, J. Kaldor, Y. Yuki, M. Carrington, T. Allen, S. Mallal, M. Altfeld, D. Heckerman, and B. Walker. Marked epitope- and allele-specific differences in rates of mutation in human immunodeficiency type 1 (HIV-1) Gag, Pol, and Nef cytotoxic T-lymphocyte epitopes in acute/early HIV-1 infection. Journal of Virology, 82:9216-9227, September 2008.
- T. Miura, M. Brockman, C. Brumme, Z. Brumme, J. Carlson, F. Pereyra, A. Trocha, M. Addo, B. Block, A. Rothchild, B. Baker, T. Flynn, A. Schneidewind, B. Li, Y. Wang, D. Heckerman, T. Allen, and B. Walker. Genetic characterization of human immunodeficiency virus type 1 in elite controllers: Lack of gross genetic defects or common amino acid changes. Journal of Virology, 82:8422-8430, September, 2008.
- T. Kuntzen, J. Timm, A. Berical, N. Lennon, A. Berlin, S. Young, B. Lee, D. Heckerman, J. Carlson, L. Reyor, M. Kleyman, C. McMahon, C. Birch, J. Schulze Zur Wiesch, T. Ledlie, M. Koehrsen, C. Kodira, A. Roberts, G. Lauer, H. Rosen, F. Bihl, A. Cerny, U. Spengler, Z. Liu, A. Kim, Y. Xing, A. Schneidewind, M. Madey, J. Fleckenstein, V. Park, J. Galagan, C. Nusbaum, B. Walker, G. Lake-Bakaar, E. Daar, I. Jacobson, E. Gomperts, B. Edlin, S. Donfield, R. Chung, A. Talal, T. Marion, B. Birren, M. Henn, T. Allen. Naturally occurring dominant resistance mutations to hepatitis C virus protease and polymerase inhibitors in treatment-naïve patients. Hepatology, 48:1769-1778, July 2008.
- C. Wang, D. Blei, and D. Heckerman. Continuous Time Dynamic Topic Models. In Proceedings of Twenty Fourth Conference on Uncertainty in Artificial Intelligence, Helsinki, Finland, UAI Press, July 2008.
- Z. Brumme., I. Tao, S. Szeto, C. Brumme, J. Carlson, D. Chan, C. Kadie, N. Frahm, C. Brander, B. Walker, D. Heckerman, and P. Harrigan. HLA-specific polymorphisms in HIV-1 Gag and their association with viral load in chronic untreated, AIDS, 22(11):1277-1286, July, 2008.
- P. Goepfert, W. Lumm, P. Farmer, P. Matthews, A. Prendergast, J. Carlson, C. Derdeyn, J. Tang, R. Kaslow, A. Bansal, K. Yusim, D. Heckerman, J. Mulenga, S. Allen, P. Goulder, and E. Hunter. Transmission of HIV-1 Gag immune escape mutations is associated with reduced viral load in linked recipients, Journal of Experimental Medicine, 205(5):1009-1017, April, 2008.
- C. Rousseau, M. Daniels, J. Carlson C. Kadie, H. Crawford, A. Prendergast, P. Matthews, D. Raugi, B. Maust G. Learn D. Nickle N. Frahm, C. Brander, B. Walker P. Goulder, T. Bhattacharya, D. Heckerman, B. Korber, and J. Mullins. Class-I driven evolution of human immunodeficiency virus type 1 subtype C proteome: Immune escape and viralload, Journal of Virology, doi:10.1128/JVI.02455-07, April 2008.
- H. Kang, N. Zaitlen, C. Wade, A. Kirby, D. Heckerman, M. Daly, and E. Eskin, Efficient Control of Population Structure in Model Organism Association Mapping, Genetics, 178:1709-1723, March, 2008.
- J. Listgarten, Z. Brumme, C. Kadie, G. Xiaojiang, B. Walker, M. Carrington, P. Goulder, and D. Heckerman. Statistical resolution of ambiguous HLA typing data. PLoS Computational Biology, 4(2): e1000016, February, 2008.
- D. Yerly, D. Heckerman, T. Allen, J. Chisholm, K. Faircloth, C. Linde, N. Frahm, J. Timm, W. Pichler, A. Cerny, and C. Brander. Increased CTL epitope variant cross-recognition and functional avidity are associated with HCV clearance. J. Virol, January 2008.
- D. Nickle, N. Jojic, D. Heckerman, V. Jojic, D. Kirovski, M. Rolland, S. Pond, J. Mullins. Comparison of immunogen designs that optimize peptide coverage: Reply to Fischer et al., PLoS Computational Biology, 4(1):e25, January 2008.
- M. Rolland, D. Heckerman, W. Deng, C. Rousseau, H. Coovadia K. Bishop, P. Goulder, B. Walker, C. Brander, J. Mullins. Broad and Gag-biased HIV-1 epitope repertoires are associated with lower viral loads. PLoS ONE, 3(1): e1424, January, 2008.
- N.
Kholiswa, C. Day, Z. Mncube, K. Nair, D. Ramduth, C. Thobakgale, E.
Moodley, S. Reddy, C. de Pierres, N. Mkhwanazi, K. Bishop, M. van der
Stok, N. Ismail, I. Honeyborne, H. Crawford, D. Kavanagh, C. Rousseau, D.
Nickle, J. Mullins, D. Heckerman, B. Korber, H. Coovadia, P. Goulder, and
B. Walker. Targeting of a CD8 T cell
Env epitope presented by HLA-B*5802 is associated with markers of HIV
disease progression and lack of selection pressure, AIDS Res Hum Retroviruses. 2008
Jan;24(1):72-82.
- J. Listgarten, N. Frahm, C. Kadie, C. Brander, D. Heckerman. A statistical framework for modeling HLA-dependent T cell response data, PLoS Computational Biology, 3(10): e188, October 2007.
- D. Heckerman, C. Kadie, and J. Listgarten. Leveraging information across HLA alleles/supertypes improves epitope prediction. J. of Comp. Bio, 14(6): 736-746, August 2007. Also appears as MSR-TR-05-127, Microsoft Research, September, 2005.
- N. Frahm, K. Yusim, T. Suscovich, S. Adams, J. Sidney, P. Hraber, H. Hewitt, Ca. Linde, D. Kavanagh, T. Woodberry, L. Henry, K. Faircloth, J. Listgarten, C. Kadie, N. Jojic, K. Sango, N. Brown, E. Pae, M. Zaman, F. Bihl, A. Khatri, M. John, S. Mallal, F. Marincola, B. Walker, A. Sette, D. Heckerman, B. Korber, C. Brander. Extensive HLA class I allele promiscuity among viral CTL epitopes. EJI, 37, August 2007.
- Z. Brumme, C. Brumme, D. Heckerman, B. Korber, M. Daniels, J. Carlson, C. Kadie, T. Bhattacharya, C. Chui, T. Mo, R. Hogg, J. Montaner, N. Frahm, C. Brander, B. Walker, P. Harrigan. Evidence of Differential HLA Class I-Mediated Viral Evolution in Functional and Accessory/Regulatory Genes of HIV-1. PLoS Pathogens, 3(7): e94, July 2007.
- J. Carlson, C. Kadie, S. Mallal, and D. Heckerman. Leveraging hierarchical population structure in discrete association studies. PLoS ONE, 2(7): e591, July 2007.
- J. Listgarten and D. Heckerman, Determining the number of non-spurious arcs in a learned DAG model: Investigation of a Bayesian and a frequentist approach. In Proceedings of Twenty Third Conference on Uncertainty in Artificial Intelligence, Vancouver, Canada, UAI Press, July 2007. Also appears as Technical Report MSR-TR-07-60, Microsoft Research, May, 2007.
- C. Rousseau, G. Learn, T. Bhattacharya, D. Nickle, D. Heckerman, S. Chetty, C. Brander, P. Goulder, B. Walker, P. Kiepiela, B. Korber, and J. Mullins. Extensive intrasubtype recombination in South African Human Immunodeficiency Virus type I subtype C infections. J Virol, 81(9): 4492-4500. May, 2007.
- D. Nickle, M. Rolland, M. Jensen, S. Pond, W. Deng, M. Seligman, D. Heckerman, J. Mullins, and N. Jojic. Coping with viral diversity in HIV vaccine design, PLoS Computational Biology, 3(4): e75, April 2007.
- T. Bhattacharya, M. Daniels, D. Heckerman, B. Foley, N. Frahm, C. Kadie, J. Carlson, K. Yusim, B. McMahon, B. Gaschen, S. Mallal, J. Mullins, D. Nickle, J. Herbeck, C. Rousseau, G. Learn, T. Miura, C. Brander, B. Walker, B. Korber. Founder effects in the assessment of HIV polymorphisms and HLA allele associations, Science, 315, 1583-1586, March 16 2007.
- P. Kiepiela, K. Ngumbela, C. Thobakgale, D. Ramduth, I. Honeyborne, E. Moodley, S. Reddy, C. de Pierres, Z. Mncube, N. Mkhwanazi, K. Bishop, M. van der Stok, K. Nair, N. Khan, H. Crawford, R. Payne, A. Leslie, J. Prado, A. Prendergast, J. Frater, N. McCarthy, C. Brander, G. Learn, D. Nickle, C. Rousseau, H. Coovadia, J. Mullins, D. Heckerman, B. Walker, and P. Goulder. CD8+ T-cell responses to different HIV proteins have discordant associations with viral load, Nature Medicine, December 17 2006.
- F. Bach, D. Heckerman, E. Horvitz, Considering cost asymmetry in learning classifiers, Journal of Machine Learning Research, 7, 1713-1741, 2006.
- N. Jojic, V. Jojic, B. Frey, C. Meek, and D. Heckerman. Using epitomes to model genetic diversity: Rational design of HIV vaccine cocktails. NIPS 2005.
- J. Goodman, D. Heckerman, and R. Rounthwaite. Stopping Spam. Scientific American, April, 2005.
- G. Shani, D. Heckerman, and R. Brafman. An MDP-based recommender system. Journal of Machine Learning Research 6: 1265-1295, 2005.
- F.
Bach, D. Heckerman, and E. Horvitz, On the Path to an Ideal
ROC Curve: Considering Cost Asymmetry in Learning Classifiers. In Proceedings of the Tenth International Workshop on Artificial
Intelligence and
- D. Chickering, D. Heckerman, and C. Meek, Large-Sample Learning of Bayesian Networks is NP-Hard. Journal of Machine Learning Research. 5: 1287-1330, 2004.
- D. Heckerman, C. Meek, and T. Richardson, Variations on Undirected Graphical Models and their Relationships. Technical Report MSR-TR-2004-95, Microsoft Research, September, 2004.
- V. Jojic, N. Jojic, C. Meek, D. Geiger, A. Siepel, D. Haussler, and D. Heckerman: Efficient approximations for learning phylogenetic HMM models from data. ISMB/ECCB (Supplement of Bioinformatics) 161-168, 2004. Also appears as Technical Report MSR-TR-2003-62, Microsoft Research, October, 2003.
- N.
Jojic, V. Jojic, and D. Heckerman, Joint discovery of
haplotype blocks and complex trait associations from SNP sequences. In
Proceedings of Twentieth Conference on
Uncertainty in Artificial Intelligence,
- B.
Thiesson, D. Chickering, D. Heckerman, and C. Meek, ARMA Time-Series
Modeling with Graphical Models. In Proceedings of Twentieth Conference on Uncertainty in Artificial
Intelligence,
- D. Heckerman, C. Meek, and D. Koller, Probabilistic Models for Relational Data. Technical Report MSR-TR-2004-30, Microsoft Research, March, 2004.
- D. Chickering and D Heckerman. Targeted Advertising with Inventory Management. Interfaces, 33:71-77, 2003. Also appears as Technical Report MSR-TR-00-49, Microsoft Research, August, 2000.
- I. Cadez, D. Heckerman, C. Meek, P. Smyth, and S. White, Visualization of Navigation Patterns on a Web Site Using Model Based Clustering, Data Mining and Knowledge Discovery, 7:399-424, 2003. Also appears as Technical Report MSR-TR-00-18, Microsoft Research, March, 2000.
- G. Hulten, D.M. Chickering, D. Heckerman. Learning Bayesian Networks from Dependency Networks: A Preliminary Study. In Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL, January 2003.
- C.
Meek, B. Thiesson, and D. Heckerman, Staged Mixture Modeling and
Boosting. In Proceedings of Eighteenth Conference on Uncertainty in Artificial
Intelligence, Edmonton, Alberta, Morgan Kaufmann, August 2002. Also appears as Tech
- C.
Kadie, C. Meek, and D. Heckerman.
CFW:
A collaborative filtering system using posteriors over weights of evidence. In Proceedings of Eighteenth Conference on Uncertainty in Artificial
Intelligence, Edmonton, Alberta, Morgan Kaufmann, August 2002. Also appears as Tech
- C. Meek, D. Chickering and D. Heckerman. Autoregressive tree models for time-series analysis. In Proceedings of the Second International SIAM Conference on Data Mining, Arlington, VA, SIAM, April, 2002.
- D. Geiger and D. Heckerman. Parameter
Priors for Directed Acyclic Graphical Models and the Characterization of
Several Probability Distributions. The
Annals of Statistics, 30: 1412-1440, 2002. Also appears as Tech
- C. Meek,
B. Thiesson, and D. Heckerman.
The
Learning-Curve Sampling Method Applied to Model-Based Clustering. Journal of Machine Learning Research, 2:397-418, 2001. Also appears as Tech
- N. Jojic, P. Simard, B. J. Frey and D. Heckerman. Learning mixtures of smooth, nonuniform deformation models for probabilistic image matching. In Proceedings of Eighth International Workshop on Artificial Intelligence and Statistics, Key West, FL, Morgan Kaufmann, January 2001.
- B.
Thiesson, C. Meek, and D. Heckerman. Accelerating
EM for Large Databases. Machine Learning,
45:279-299, 2001. Also appears
as Tech
- D.
Chickering and D. Heckerman. A
Decision-Theoretic Approach to Targeted Advertising. In
Proceedings of Sixteenth Conference
on Uncertainty in Artificial Intelligence, Stanford, CA, Morgan
Kaufmann, July 2000. Also
appears as Tech
- D.
Heckerman, D. Chickering, C. Meek, R. Rounthwaite, C. Kadie. Dependency
Networks for Density Estimation, Collaborative Filtering, and Data
Visualization. Journal
of Machine Learning Research. 1:49-75, 2000. Also appears as Tech
- D. Heckerman and D. Chickering. A Comparison of Scientific and Engineering Criteria for Bayesian Model Selection. Statistics and Computing, 10:55-62, 2000. Also appears as Technical Report MSR-TR-96-12, Microsoft Research, June, 1996 (revised November, 1996).
- D.
Chickering and D. Heckerman. Fast
Learning from Sparse Data.
In Proceedings of Fifteenth
Conference on Uncertainty in Artificial Intelligence, Stockholm,
Sweden, Morgan Kaufmann, August 1999.
Also appears as Tech
- D.
Heckerman, C. Meek, and G. Cooper A
Bayesian Approach to Causal Discovery. In C. Glymour and G. Cooper,
editors, Computation, Causation, and Discovery, pages 141-165. MIT Press, Cambridge, MA,
1999. Also appears as Tech
- D. Geiger, D. Heckerman, H. King, C.
Meek. Stratified
Exponential Families: Graphical Models and Model Selection. The
Annals of Statistics, 29:505-529, 2001. Also appears as Tech
- S. T. Dumais, J. Platt, D. Heckerman and M. Sahami. Inductive Learning Algorithms and Representations for Text Categorization. (Word file). Proceedings of ACM-CIKM98, November, 1998.
- M.
Sahami, S. Dumais, D. Heckerman, and E. Horvitz. A
Bayesian Approach to Filtering Junk E-mail. AAAI'98 Workshop on
Learning for Text Categorization, July 27, 1998,
- D. Heckerman and E. Horvitz. Inferring Informational Goals from Free-Text Queries. In Proceedings of Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, Morgan Kaufmann, July 1998.
- E. Horvitz, J. Breese, D. Heckerman, D. Hovel, and K. Rommelse. The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. In Proceedings of Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, Morgan Kaufmann, July 1998.
- J.
Breese, D. Heckerman, C. Kadie Empirical
Analysis of Predictive Algorithms for Collaborative Filtering. In Proceedings of Fourteenth Conference on Uncertainty in Artificial
Intelligence, Madison, WI, Morgan Kaufmann, July 1998. Also appears as Tech
- M.
Meila and D. Heckerman An
Experimental Comparison of Several Clustering and Initialization Methods. Machine Learning. 42:9-29, 2001. Also appears as Tech
- B.
Thiesson, C. Meek, D. Chickering, D. Heckerman. Computationally
Efficient Methods for Selecting Among Mixtures of Graphical Models. In J. M. Bernardo, J. O. Berger, A.
P. Dawid, and A. F. M. Smith, editors, Bayesian Statistics 6, pages
631-656, Oxford University Press, Oxford, 1999. Also appears as Tech
- D.
Heckerman and C. Meek. Models
and Selection Criteria for Regression and Classification. In Proceedings of Thirteenth Conference on Uncertainty in Artificial
Intelligence, Providence, RI, pages 223-228. Morgan Kaufmann, August
1997. Also appears as Tech
- D.
Chickering, D. Heckerman, C. Meek.
A
Bayesian Approach to Learning Bayesian Networks with Local Structure. In Proceedings of Thirteenth Conference on Uncertainty in Artificial
Intelligence, Providence, RI, pages 80-89. Morgan Kaufmann, August
1997. Also appears as Tech
- C. Meek and D. Heckerman. Structure and parameter learning for causal independence and causal interaction models. In Proceedings of Thirteenth Conference on Uncertainty in Artificial Intelligence, Providence, RI, pages 366-375. Morgan Kaufmann, August 1997.
- D.
Heckerman and C. Meek Embedded
Bayesian Network Classifiers Tech
- P. Smyth, D. Heckerman, M. Jordan. Probabilistic Independence Networks for Hidden Markov
Probability Models. Neural Computation, 9:227-269, 1997. Also
appears as Tech
- D.
Chickering and D. Heckerman. Efficient
Approximations for the Marginal Likelihood of Bayesian Networks With
Hidden Variables. Machine Learning, 29:181-212,
1997. Also appears as Tech
- D.
Geiger, D. Heckerman, and C. Meek. Asymptotic
Model Selection for Directed Networks with Hidden Variables. In Proceedings of Twelfth Conference on Uncertainty in Artificial
Intelligence, Portland, OR, pages 283-290. Morgan Kaufmann, August
1996. Also appears as Tech
- J.
Breese and D. Heckerman. Decision-theoretic
troubleshooting: A framework for repair and experiment. In Proceedings of Twelfth Conference on Uncertainty in Artificial
Intelligence, Portland, OR, pages 124-132. Morgan Kaufmann, August
1996. Also appears as Tech
- J. Breese and D. Heckerman. Decision-theoretic case-based reasoning. IEEE Transactions on Systems, Man, and Cybernetics, 26:838-842, 1996. Local copy. Also appears as MSR-TR-95-03, Microsoft Research, November, 1994 (revised August, 1995).
- D.
Geiger, D. Heckerman. A
Characterization of the Dirichlet Distribution Through Global and Local
Independence. The Annals of Statistics,
25:1344-1369, 1997. Also appears
as Tech
- D. Geiger and D. Heckerman. Beyond Bayesian networks: Similarity networks and Bayesian multinets. Artificial Intelligence, 82:45-74, 1996.
- D. Heckerman, J. Breese. Causal Independence for Probability Assessment and Inference Using Bayesian Networks. IEEE Transactions on Systems, Man, and Cybernetics, 26:826-831, 1996. Local copy. Also appears as Technical Report MSR-TR-94-08, Microsoft Research, March, 1994 (revised October, 1995).
- D.
Geiger, D. Heckerman. A Characterization
of the Bivariate Normal-Wishart Distribution. Tech
- D.
Heckerman, R. Shachter. Decision-Theoretic
Foundations for Causal Reasoning.
Journal of Artificial
Intelligence Research, 3:405-430, 1995. Also appears a Tech
- D. Heckerman, D. Geiger, D.
Chickering. Learning
Bayesian networks: The Combination of Knowledge and Statistical Data. Machine
Learning, 20:197-243, 1995.
Also appears as Tech
- D. Chickering, D. Geiger, and D. Heckerman. On finding a cycle basis with a shortest maximal cycle. Information Processing Letters, 54:55-58, 1995.
- D. Heckerman. A Bayesian approach to learning causal networks. In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, Quebec, pages 285-295. Morgan Kaufmann, August 1995. Also appears as Technical Report MSR-TR-95-04, Microsoft Research, March, 1995.
- D. Geiger and D. Heckerman. A characterization of the Dirichlet distribution with applications to learning Bayesian networks. In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, Quebec, pages 196-207. Morgan Kaufmann, August 1995.
- D. Heckerman and R. Shachter. A definition and graphical representation for causality. In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, Quebec, pages 262-273. Morgan Kaufmann, August 1995.
- D. Heckerman and D. Geiger. Learning Bayesian networks: A unification for discrete and Gaussian domains. In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, Quebec, pages 274-284. Morgan Kaufmann, August 1995.
- J. Breese and D. Heckerman. Decision-theoretic case-based reasoning. In Proceedings of Fifth Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, FL. Society for Artificial Intelligence in Statistics, January 1995.
- D. Heckerman and R. Shachter. A decision-based view of causality. In Proceedings of Fifth Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, FL. Society for Artificial Intelligence in Statistics, January 1995.
- D. Heckerman, J. Breese, K. Rommelse. Decision-theoretic troubleshooting. CACM, 38:49-57, 1995.
· D. Heckerman, J. Breese, and K. Rommelse. Troubleshooting under uncertainty. In Proceedings of Fifth International Workshop on Principles of Diagnosis, New Paltz, NY, pages 121-130, October 1994. Also appears as Technical Report MSR-TR-94-07, Microsoft Research, March, 1994.
· D. Heckerman, D. Geiger, and D. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. In Proceedings of Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, WA, pages 293-301. Morgan Kaufmann, July 1994.
· D. Geiger and D. Heckerman. Learning Gaussian networks. In Proceedings of Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, WA, pages 235-243. Morgan Kaufmann, July 1994.
· D. Heckerman and J. Breese. A new look at causal independence. In Proceedings of Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, WA, pages 286-292. Morgan Kaufmann, July 1994.
· D. Heckerman and R. Shachter. A decision-based view of causality. In Proceedings of Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, WA, pages 302-310. Morgan Kaufmann, July 1994.
· D. Geiger and D. Heckerman. Inference algorithms for similarity networks. In Proceedings of Ninth Conference on Uncertainty in Artificial Intelligence, Washington, DC, pages 326-334. Morgan Kaufmann, July 1993.
· D. Heckerman. Causal independence for knowledge acquisition and inference. In Proceedings of Ninth Conference on Uncertainty in Artificial Intelligence, Washington, DC, pages 122-127. Morgan Kaufmann, July 1993.
· D. Heckerman and M. Shwe. Diagnosis of multiple faults: A sensitivity analysis. In Proceedings of Ninth Conference on Uncertainty in Artificial Intelligence, Washington, DC, pages 80-87. Morgan Kaufmann, July 1993.
· D. Heckerman and E. Horvitz. Problem formulation as the reduction of a decision model. In Proceedings of Ninth Conference on Uncertainty in Artificial Intelligence, Washington, DC, pages 80-87. Morgan Kaufmann, July 1993.
· D. Heckerman, E. Horvitz, and B. Middleton. An approximate nonmyopic computation for value of information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15:292-298, 1993.
· D. Heckerman, E. Horvitz, and B. Nathwani. Toward normative expert systems: Part I. The Pathfinder project. Methods of Information in Medicine, 31:90-105, 1992.
· D. Heckerman and B. Nathwani. Toward normative expert systems: Part II. Probability-based representations for efficient knowledge acquisition and inference. Methods of Information in Medicine, 31:106-116, 1992. Also in van Bemmel, J., McCray, A., editors, Yearbook of Medical Informatics, pages 430-440. International Medical Informatics Association, Rotterdam, The Netherlands, 1993.
· D. Heckerman. The certainty-factor model. In S. Shapiro, editor, Encyclopedia of Artificial Intelligence, Second Edition, pages 131-138. Wiley, New York, 1992.
· D. Heckerman and E. Shortliffe. From certainty factors to belief networks. Artificial Intelligence in Medicine, 4:35-52, 1992.
· D. Heckerman and B. Nathwani. An evaluation of the diagnostic accuracy of Pathfinder. Computers and Biomedical Research, 25:56-74, 1992.
· M. Shwe, B. Middleton, D. Heckerman, M. Henrion, E. Horvitz, H. Lehmann, and G. Cooper. Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base: Part 1. The probabilistic model and inference algorithms. Methods of Information in Medicine, 30:241-255, 1991.
· B. Middleton, M. Shwe, D. Heckerman, M. Henrion, E. Horvitz, H. Lehmann, and G. Cooper. Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base: Part 2. Evaluation of diagnostic performance. Methods of Information in Medicine, 30:256-267, 1991.
· D. Heckerman, E. Horvitz, and B. Middleton. An approximate nonmyopic computation for value of information. In Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence, Los Angeles, CA, pages 135-141. Morgan Kaufmann, July 1991.
· D. Geiger and D. Heckerman. Advances in probabilistic reasoning. In Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence, Los Angeles, CA, pages 118-126. Morgan Kaufmann, July 1991.
· D. Heckerman. Probabilistic Similarity Networks. MIT Press, Cambridge, MA, 1991.
· D. Heckerman. Probabilistic similarity networks. Networks, 20:607-636, 1990.
· B. Nathwani, D. Heckerman, E. Horvitz, and T. Lincoln. Integrated expert systems and videodisc in surgical pathology: An overview. Human Pathology, 21:11-27, 1990.
· D. Heckerman. Similarity networks for the construction of multiple-fault belief networks. In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, Boston, MA, pages 32-39. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, July 1990. Also in P. Bonissone, M. Henrion, L. Kanal, and J. Lemmer, editors, Uncertainty in Artificial Intelligence 6, pages 51-64. North-Holland, New York, 1990.
· D. Heckerman and E. Horvitz. Problem formulation as the reduction of a decision model. In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, Boston, MA, pages 82-89. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, July 1990. Also in P. Bonissone, M. Henrion, L. Kanal, and J. Lemmer, editors, Uncertainty in Artificial Intelligence 6, pages 159-170. North-Holland, New York, 1990.
· D. Geiger and D. Heckerman. Separable and transitive graphoids. In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, Boston, MA, pages 538-545. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, July 1990.
· H. Suermondt, G. Cooper, and D. Heckerman. A combination of cutset conditioning with clique-tree propagation in the Pathfinder system. In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, Boston, MA, pages 273-279. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, July 1990.
· D. Heckerman. A tractable algorithm for diagnosing multiple diseases. In Proceedings of the Fifth Workshop on Uncertainty in Artificial Intelligence, Windsor, ON, pages 174-181. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1989. Also in M. Henrion, R. Shachter, L. Kanal, and J. Lemmer, editors, Uncertainty in Artificial Intelligence 5, pages 163-171. North-Holland, New York, 1990.
· D. Heckerman, J. Breese, and E. Horvitz. The compilation of decision models. In Proceedings of Fifth Workshop on Uncertainty in Artificial Intelligence, Windsor, ON, pages 162-173. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1989.
· D. Heckerman, E. Horvitz, and B. Nathwani. Update on the Pathfinder project. In Proceedings of the Thirteenth Symposium on Computer Applications in Medical Care, Washington, D, pages 203-207. IEEE Computer Society Press, Silver Spring, MD, November 1989.
· E. Horvitz, G. Cooper, and D. Heckerman. Reflection and action under scarce resources: Theoretical principles and empirical study. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Detroit, MI, pages 1121-1127. Morgan Kaufmann, San Mateo, CA, August 1989.
· E. Horvitz, D. Heckerman, K. Ng, and B. Nathwani. Heuristic abstraction in the decision-theoretic Pathfinder system. In Proceedings of the Thirteenth Symposium on Computer Applications in Medical Care, Washington, DC, pages 178-182. IEEE Computer Society Press, Silver Spring, MD, November 1989.
· D. Heckerman. An empirical comparison of three inference methods. In Proceedings of the Fourth Workshop on Uncertainty in Artificial Intelligence, Minneapolis, MN, pages 158-169. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1988. Also in R. Shachter, T. Levitt, L. Kanal, and J. Lemmer, editors, Uncertainty in Artificial Intelligence 4, pages 283-302. North-Holland, New York, 1990.
· D. Heckerman and E. Horvitz. On the expressiveness of rule-based systems for reasoning under uncertainty. In Proceedings AAAI-87 Sixth National Conference on Artificial Intelligence, Seattle, WA, pages 121-126. Morgan Kaufmann, San Mateo, CA, July 1987. Local copy.
· D. Heckerman and H. Jimison. A perspective on confidence and its use in focusing attention during knowledge acquisition. In Proceedings of the Third Workshop on Uncertainty in Artificial Intelligence, Seattle, WA, pages 123-131. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, July 1987. Also in L. Kanal, T. Levitt, and J. Lemmer, editors, Uncertainty in Artificial Intelligence 3, pages 123-131. North-Holland, New York, 1989.
· R. Shachter and D. Heckerman. Thinking backward for knowledge acquisition. AI Magazine, 8:55-63, 1987. Local copy.
· D. Heckerman. An axiomatic framework for belief updates. In Proceedings of the Second Workshop on Uncertainty in Artificial Intelligence, Philadelphia, PA, pages 123-128. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1986. Also in L. Kanal and J. Lemmer, editors, Uncertainty in Artificial Intelligence 2, pages 11-22. North-Holland, New York, 1988.
· D. Heckerman and E. Horvitz. The myth of modularity in rule-based systems. In Proceedings of the Second Workshop on Uncertainty in Artificial Intelligence, Philadelphia, PA, pages 115-121. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1986. Also in L. Kanal and J. Lemmer, editors, Uncertainty in Artificial Intelligence 2, pages 23-34. North-Holland, New York, 1988.
· D. Heckerman and R. Miller. Towards a better understanding of the INTERNIST-1 knowledge base. In Proceedings of Medinfo, Washington, DC, pages 27-31. North-Holland, New York, October 1986.
· E. Horvitz, D. Heckerman, and C. Langlotz. A framework for comparing alternative formalisms for plausible reasoning. In Proceedings AAAI-86 Fifth National Conference on Artificial Intelligence, Philadelphia, PA, pages 210-214. Morgan Kaufmann, San Mateo, CA, August 1986. Local copy.
· E. Horvitz, D. Heckerman, B. Nathwani, and L. Fagan. The use of a heuristic problem-solving hierarchy to facilitate the explanation of hypothesis-directed reasoning. In Proceedings of Medinfo, Washington, DC, pages 27-31. North-Holland, New York, October 1986.
· E. Horvitz and D. Heckerman. The inconsistent use of measures of certainty in artificial intelligence research. In Proceedings of the Workshop on Uncertainty and Probability in Artificial Intelligence, Los Angeles, CA. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1985. Also in L. Kanal and J. Lemmer, editors, Uncertainty in Artificial Intelligence, pages 137-151. North-Holland, New York, 1986.
· D. Heckerman. Probabilistic interpretations for MYCIN's certainty factors. In Proceedings of the Workshop on Uncertainty and Probability in Artificial Intelligence, Los Angeles, CA, pages 9-20. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1985. Also in L. Kanal. and J. Lemmer, editors, Uncertainty in Artificial Intelligence, pages 167-196. North-Holland, New York, 1986.
· D. Heckerman, R. Rosenbaum, S. Putterman, and G. Williams. Pressure release superleak sound modes in He II. Journal of Low Temperature Physics, 38:629, 1980.
· D. Heckerman, S. Garrett, G.A. Williams, and P. Weidman. Surface tension restoring forces on gravity waves in narrow channels. Physics of Fluids, 22, 1979.
· S. Putterman, D. Heckerman, R. Rosenbaum, and G. Williams. Superfluid two-phase sound. Physics Review Letters, 42:580, 1979.
· R. Rosenbaum, G. Williams, D. Heckerman, J. Marcus, D. Scholler, J. Maynard, and I. Rudnick. Surface tension sound in superfluid helium films adsorbed on alumina powder. Journal of Low Temperature Physics, 37:663, 1979.
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