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THOMAS RICHARDSON

4502 Greenwood Avenue North Seattle, WA 98103 e-mail: tsr@stat.washington.edu

EDUCATION

CARNEGIE MELLON UNIVERSITY Ph.D. Logic, Computation & Methodology M.Sc. Logic & Computation MERTON COLLEGE B.A.(Hons), Mathematics & Philosophy

Pittsburgh, USA August, 1996 August, 1995 Oxford, UK August, 1992

THESES

Ph.D. Thesis: Feedback Models: Interpretation and Discovery M.Sc. Thesis: Properties of Cyclic Graphical Models

EMPLOYMENT

UNIVERSITY OF WASHINGTON Department of Statistics Full Professor Associate Professor Assistant Professor Department of Economics Adjunct Associate Professor UNIVERSITY OF WARWICK Lecturer ZENTRUM F?R UMFRAGEN, METHODEN

UND ANALYSEN

Seattle, USA Fall 2007Fall 2000-Fall 2007 Fall 1996-Fall 2000 Winter 2007-

Coventry, UK Fall 1999-Fall 2000 Mannheim, Germany July, 1999

(ZUMA)

Gastprofessor -1-

RESEARCH GRANTS

Co-PI: NIH Predicting bone formation induced by mechanical loading using agent based models (PI: S. Srinivasan, Univ. of Washington) Co-PI: CSSS Seed grant Improved Confidence Intervals for Subvectors in IV Regression with Weak Identification (PI: Eric Zivot, UW Economics) PI: NSF-DMS Graphical and Algebraic Models for Multivariate Categorical Data (Collaborative grant with M. Drton, at University of Chicago) Co-PI: NIH: Analytic methods for HIV treatment and co-factor effects Harvard School of Public Health (J.Robins, PI) (UW Subcontract) PI: RRF Grant: Likelihood inference in regression systems in the presence of multimodality PI: CSSS Seed Grant: Intonation in Unangas (Western Aleut), an endangered Alaskan language, (J. Wegelin and A. Taff). Co-PI: DARPA-CFI: Graphical Models: Mixing Knowledge and Data-Driven Techniques. (Jeff Bilmes, PI). PI: NSF-DMS Grant Graphical Markov Models with Interpretable Structure

Dec 2006-Nov 2008

Dec 2006-Dec 2007

July 2006-July 2009

June 2005-May 2010

Sept 2003-Sept 2004

Sept 2001-July 2002

Summer 2001

Nov 1999-Aug 2003

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PI: EPA-NRCSE Grant Graphical Models for statistical and causal inferences about mortality from airborne particles (PM) Co-PI: NSF-DMS Grant Graphical Markov Models, Structural Equation Models, and Related Models of Multivariate Dependence PI: Michael Perlman PI: RRF Grant Bayesian software tools for causal model search AWARDS & FELLOWSHIPS INSTITUTE FOR ADVANCED STUDIES Fellowship CENTER FOR ADVANCED STUDIES IN THE BEHAVIORAL SCIENCES Fellowship ISAAC NEWTON INSTITUTE Rosenbaum Fellow 20TH CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE Outstanding Student Paper Award (as co-author) 12TH CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE Outstanding Student Paper Award VISITING SENIOR RESEARCH FELLOW

1999-2002

June 1997-June 2000

June 1997-June 1999

Jesus College, Oxford Jan-June 2008 University of Bologna Sept-Dec 2007 Stanford University, Palo Alto, USA January-June 2004 Cambridge, UK July - Dec 1997 Banff, Canada July 2004

Portland, USA Aug 1996

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PHD STUDENTS SUPERVISED Defended: 2006 Erica Moodie (Biostatistics) 2005 Sanjay Chaudhuri (with M. Perlman) 2004 Mathias Drton (with M. Perlman) 2003 Ayesha Ali 2002 Jacob Wegelin (with P. Sampson) 1999 Greg Ridgeway (with D. Madigan) MSC STUDENTS 1999 Jake Brutlag P HD COMMITTEES Accounting; Biostatistics; Computer Science; Economics; Electrical Engineering; Finance; Fisheries; Forestry; Mechanical Engineering; Statistics EDITING Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (with R. Dechter) Associate Editor for The Journal of the Royal Statistical Society Series B. Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (with T. Jaakkola) Guest Editor for Statistics and Computing

Affiliation McGill University Canada National University of Singapore University of Chicago University of Guelph Canada UC Davis RAND

Google

July 2006

Aug 1999-July 2004

Jan 2001

Aug 1997-Aug 1999

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REVIEWING

STATISTICS: Annals of Statistics; Bernoulli; Biometrika; Communications in Statistics; Games and Economic Behavior; International Journal of Biostatistics, International Statistical Review; Journal of Multivariate Analysis; Journal of the Royal Statistical Society; Probability Theory and Related Fields; Scandinavian Journal of Statistics; Statistical Modelling; Statistica Sinica; Statistics and Computing; Learning in Graphical Models. SOCIAL SCIENCE: Erkenntnis; Sociological Methodology; Sociological Methods and Research; Behaviormetrika

COMPUTER SCIENCE: Conference on AI and Statistics; International Joint Conference on AI; Journal of AI Research; Knowledge Discovery and Data Mining; Machine Learning; Neural and Information Processing Systems; IEEE Transactions on Knowledge and Data Engineering; Oxford University Press; Conference on Uncertainty in Artificial Intelligence; IEEE Transactions on Signal Processing. MATHEMATICS: Journal of Combinatorial Theory GRANT PROPOSALS AGENCIES: NSF; EPSRC (UK); RRF

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CONFERENCE ORGANIZING

WNAR/IMS MEETING IMS Program Chair 23RD CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE General Co-Chair 22ND CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE Program Co-Chair IMS/BERNOULLI SOCIETY MEETING Session organizer WNAR/IMS MEETING Session organizer ASSOCIATION FOR AI AND STATISTICS Conference organizer (with T. Jaakkola, MIT) FIELDS INSTITUTE Seminar organizer (with P.Spirtes, CMU) Causal Structure and Conditional Independence IMS CONFERENCE ON GRAPHICAL MODELS Webmaster

June 2007

July 2007

July 2006

Barcelona, Spain July 2004 Los Angeles, USA June 2002 Fort Lauderdale, USA Jan 2001 Toronto, Canada Oct 1999

Seattle, USA June 1997 January 2001-

ADVISORY PANELS

SOCIETY FOR AI AND STATISTICS

HARVARD SCHOOL OF PUBLIC HEALTH Causal Epidemiology program

January 2001

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PROGRAM COMMITTEES

CONFERENCE ON UNCERTAINTY IN AI CONFERENCE ON KNOWLEDGE DISCOVERY

AND DATABASES

2000-05 2000

INTERNATIONAL WORKSHOP ON AI AND STATISTICS INTERNATIONAL JOINT CONFERENCE ON AI CONFERENCE ON NEURAL AND INFORMATION PROCESSING SYSTEMS UW SERVICE Acting Director, Center for Statistics and the Social Sciences Associate Director, Center for Statistics and the Social Sciences Faculty Field Tour Faculty Fellows Program

1999,2005

1999, 2002 1998-2000, 2002, 2005,2006 July 2006-Sept 2007 June 2005-June 2006 June 1998 September 1996

DEPT SERVICE

Chair, Pedagogy Committee Computing Committee Graduate Admissions Committee Ph.D. Applied Prelim Committee M.Sc. Applied Exam Committee Stochastic Modelling Prelim Committee Computing Prelim Committee Applied Prelim Committee Executive Committee, Center for Statistics and the Social Sciences Search Committees, Center for Statistics and the Social Sciences Web-developer for UIF Proposal: Center for Statistics and the Social Sciences -7-

2002-2003 1998-1999, 2000 1998,2005,2006 2002-2004, 2006 2005 1997, 1998 1997, 1999 1998, 2003 September 20032005-6 (Chair) 2001-2003 April-June 1999

PRESENTATIONS

Cornell University Statistics Department Seminar Institute for Mathematics and its Applications, Minneapolis Invited Talk SAMSI Workshop on Random Matrices Invited Talk Harvard School of Public Health Biostatistics Department Seminar AMS Meeting, San Antonio Texas Invited Talk Penn State Statistics Department Department Seminar Graphical Models Workshop in Sm?gen Invited Talk Joint Statistics Meeting Invited Talk UW Statistics Department Seminar Duke/SAMSI Latent Variable in the Social Sciences Meeting Joint Statistics Meeting Invited Discussant

March 2007

March 2007

November 2006

October 2006

January 2006

November 2005

September 2005

August 2005

February 2005 September 2004

Toronto, Canada August 2004

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Stanford University, Statistics Department Department Seminar UC Berkeley, Mathematics Department Algebraic Statistics Workshop Center for Advanced Studies in the Behavioral Sciences Seminar Social Epidemiology, Health Disparities Symposium UW, CSSS seminar UW, Electrical Engineering Graphical Models Seminar UW, Statistics Data Mining Seminar IMS Conference (on behalf of M. Drton)

Palo Alto, California May 2004 Berkeley, California May 2004 Palo Alto, California Feb 2004

May 2003

March, 2003 March 2003

Fall 2002 Banff, Canada August 2002 April 2002 London, UK December 2001 Prague, Czech Rep. November 2001 Prague, Czech Rep. November 2001

Univ. of Washington, CSSS Seminar Royal Statistical Society, Read paper. Institute of Information Theory and Automation, (UTIA), School of Economics (VSE),

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Causal Inference Conference

Snowbird, Utah August 2001

Center for Language and Speech Processing (CLSP), EE Dept., Johns Hopkins European Science Foundation (ESF), Highly Structured Stochastic Systems (HSSS), Closing Workshop Department of Statistics, University of Washington Center for Statistics and the Social Sciences, University of Washington Bernoulli-RIKEN Meeting on Neural Networks and Learning, RIKEN (Institute of Physical and Chemical Research) Bernoulli/IMS Joint Meeting Invited speaker University of Lancaster, UK, Department of Mathematics, Danish Agricultural Sciences Institute

Baltimore, Maryland August 2001 Luminy, France, November 2000

Seattle, Washington November 2000 Seattle, Washington November 2000 Tokyo, Japan October 2000

Guanajuato, Mexico May 2000 Lancaster, UK May 2000 Foulum, Denmark April 2000

University of Aalborg, Department of Mathematical Sciences, University of Warwick, UK, Department of Statistics,

Aalborg, Denmark April 2000 March 2000

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Imperial College, University of London, Department of Statistics, ESF HSSS Conference on Graphical Models Invited speaker

March 2000

Munich, Germany March 2000

Dept. of Statistics, University of Toronto Departmental seminar

Toronto, Canada November 1999

International Statistical Institute Conference Invited speaker IMS-WNAR Meeting Invited discussant CALD Seminar, Robotics Dept, CMU Invited talk Conference on AI & Statistics Speaker, plenary session ESF HSSS Workshop on Structural Learning in Graphical Models Invited speaker Joint Statistical Meetings, ASA/IMS Invited speaker Conference on Automated Learning and Discovery (ConALD) Invited speaker Valencia Meeting on Bayesian Statistics Invited discussant -11-

Helsinki, Finland August 1999 Seattle, USA June 1999 Pittsburgh, USA February 1999 Fort Lauderdale, USA January 1999 Tirano, Italy September 1998

Dallas, USA August 1998 Pittsburgh, USA June 1998

Valencia, Spain May 1998

Dept. of Statistics, University of Washington Departmental seminar Working Group on Model Based Clustering, Dept. of statistics, University of Washington Invited talk Newton Institute Conference on Bayesian Methods Dept. of Statistics, University of Oxford Departmental seminar

Seattle, USA March 1998

Seattle, USA February 1998

Cambridge, UK December 1997 Oxford, UK November 1997

Dept. of Statistics, University of Warwick Departmental seminar ESF HSSS Workshop on Multivariate Research with Latent Variables Invited speaker Human Communications Research Center, University of Edinburgh Invited talk Dept. of Statistics, University College London Departmental seminar StatsLab, University of Cambridge Departmental seminar

Coventry, UK November 1997

Wiesbaden, Germany November 1997

Edinburgh, UK November 1997

London, UK November 1997

Cambridge, UK November 1997

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Conference on Stochastic Model Building, Duke University Invited speaker Newton Institute Conference on Graphical Models Invited speaker Santa Fe Institute Conference on Inferential problems in the analysis of treatment effects UW/Microsoft Research Workshop on Data Mining Invited participant IMS Conference on Graphical Models Invited talk Law School, University of Washington Invited talk University of Tampere Invited talk Conference on Artificial Intelligence and Statistics International Association for Statistical Computing (IASC) Invited speaker Working Group on Model Based Clustering, Dept. of Statistics, University of Washington. Invited talk -13-

Durham, USA October 1997

Cambridge, UK August 1997

Santa Fe, USA July 1997

Seattle, USA July 1997

Seattle, USA June 1997 Seattle, USA May 1997

H?meenlinna, Finland April 1997 Fort Lauderdale, USA January 1997 Pasadena, USA February 1997

Seattle, USA February 1997

12th Conference on Uncertainty in Artificial Intelligence Speaker, plenary session ESF HSSS Workshop on Association Models with Latent Variables Invited speaker Dept. of Statistics, University of Washington 11th Symposium on Computational Statistics (COMPSTAT) Speaker, section on Model Fitting SHORT COURSES Together with N.Wermuth, taught the University of Ume?, Winter Conference Course (4 days). Taught a five day course on Bayesian Networks, for the Complex Systems Group, Computer Science Dept., University of Helsinki

Portland, USA August 1996

Wiesbaden, Germany July 1996

Seattle, USA April 1996 Vienna, Austria August 1994

Bjorgafjall March 2006

Helsinki, Finland April 1997

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BIBLIOGRAPHY

Principal author(s) are underlined.

PAPERS IN M. Drton, T.S. Richardson (2008). Binary Models for Marginal REFEREED Independence. Accepted for publication in Journal of the Royal Statistical Society Series B. JOURNALS S. Chaudhuri, M. Drton, T. S. Richardson (2007). Estimation of a Covariance Matrix with Zeros. Biometrika 94(1), pp. 199-216(18). E. Moodie, T.S Richardson, D. Stephens (2007). Demystifying Optimal Dynamic Treatment Regimes. Accepted for publication in Biometrics. A. Glynn, J. Wakefield, M. Handcock, T. S. Richardson (2007) Alleviating Linear Ecological Bias and Optimal Design with Subsample Data. Accepted for publication in Journal of the Royal Statistical Society Ser. A. S. Srinivasan, B. J. Ausk, S. L. Poliachik, S. E. Warner, T. S. Richardson, T. S. Gross (2007) Rest-Inserted Loading Rapidly Amplifies the Response of Bone to Small Increases in Strain and Load Cycles. Accepted for publication in Journal of Applied Physiology J. A. Wegelin, A. Packer, and T. S. Richardson (2006). Latent models for cross-covariance. Journal of Multivariate Analysis, 97(1): 79-102. M. Drton and T. S.Richardson (2004). Multimodality of the likelihood in the bivariate seemingly unrelated regression model. Biometrika 91(2): 383-392. T.S. Richardson (2003). Markov Properties for Acyclic Directed Mixed Graphs. The Scandinavian Journal of Statistics, March 2003, vol. 30, no. 1, pp. 145-157(13). M. Banerjee and T.S. Richardson (2003). On dualization of graphical Gaussian models; a correction. The Scandinavian Journal of Statistics. March 2003, vol. 30, 817-820. -15-

S. Lauritzen and T.S. Richardson (2002). Chain graph models and their causal interpretations (with discussion). Journal of the Royal Statistical Society Series B. 64(3), 321-363. T.S. Richardson and P.Spirtes (2002). Ancestral graph Markov models. Annals of Statistics. 30, 962-1030 M. Townsend and T.S. Richardson (2002). Probability and Statistics in the Legal Curriculum: A Case Study in Disciplinary Aspects of Interdisciplinarity. Duquesne Law Review 40(3), pp.447-488. T. R. Hammond, G. L. Swartzman, T. S. Richardson (2001). Bayesian estimation of fish school cluster composition applied to a Bering Sea acoustic survey. ICES Journal of Marine Science, Vol. 58, No. 6, Nov 2001, pp. 1133-1149 J. Brutlag and T.S. Richardson (1999). A Block Sampling Approach to Distinct Value Estimation. Journal of Computational and Graphical Statistics. 11 ( 2), pp.389 – 404 R.Scheines, C.Glymour, P.Spirtes, C.Meek and T.S. Richardson (1998). The TETRAD Project: Constraint Based Aids to Model Specification. (with discussion) Multivariate Behavioral Research. 33(1) pp.65-118. P.Spirtes, T.S. Richardson, C.Meek, R. Scheines, C. Glymour (1998). Using Path Diagrams as a Structural Equation Modelling Tool. Sociological Methods and Research, 27 (2), pp.182-225. T.S. Richardson (1997). A Characterization of Markov Equivalence for Directed Cyclic Graphs. International Journal of Approximate Reasoning, 17, 2/3 (Aug-Oct. 97), pp.107-162,.

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G.Cooper, C.Aliferis, R.Ambrosino, J.Aronis, B.Buchanan, R.Caruana, M.Fine, C.Glymour, G.Gordon, B.Hanusa, J.Janosky, C.Meek, T.Mitchell, T.S.Richardson, P.Spirtes (1997). Artificial Intelligence and Medicine, 9, pp.107-138. REFEREED A. Ali, T. S. Richardson, P. Spirtes, J. Zhang. (2005). Towards CONFERENCE characterizing Markov equivalence classes for directed acyclic graphs with latent variables. Proceedings of the Twenty-First Conference on PAPERS Uncertainty in Artificial Intelligence. (F. Bacchus and T. Jaakkola Eds), p.10-17 M. Drton, T.S. Richardson (2004). Iterative Conditional Fitting for Gaussian Ancestral Graph Models. Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence, 130-137. (Outstanding student paper award). M. Drton, T.S. Richardson (2003). A new algorithm for maximum likelihood estimation in Gaussian graphical models for marginal independence. Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, pp. 184-191 S. Chaudhuri, T.S, Richardson (2003). Using the structure of dAn Evaluation of

Machine-Learning Methods for Predicting Pneumonia Mortality.

connecting paths as a qualitative measure of the strength of dependence. Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, 116-123. A. Ali, T.S. Richardson (2002). Markov equivalence classes for maximal ancestral graphs. In Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intellgience. pp.1-9. A. Ali, A. Murua, T.S. Richardson, S. Roy (2001). A Comparison of Traditional Methods and Sequential Bayesian Methods for Blind Deconvolution Problems. 27 pp. Proceedings, EUSIPCO 2002.

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J. A. Wegelin, T.S. Richardson (2001). Cross-covariance modelling via DAGs with hidden variables. Proceedings of the 17th Conference on Uncertainty and Artificial Intelligence. pp.546-553 T.S. Richardson, H. Bailer and M. Banerjee (1999). Tractable Structure Search in the Presence of Latent Variables. In Proceedings of Artificial Intelligence and Statistics ‘99 (D. Heckerman and J. Whittaker, eds.), Morgan Kaufmann, San Francisco, CA, pp.142-151. G. Ridgeway, D. Madigan, and T.S. Richardson (1999). Boosting Methodology for Regression Problems. In Proceedings of Artificial Intelligence and Statistics ‘99 (D. Heckerman and J. Whittaker, eds.), Morgan Kaufmann, San Francisco, CA, pp. 152-161. G.Ridgeway, D.Madigan, T.S. Richardson, and J.O'Kane (1998). Interpretable Boosted Naive Bayes Classification. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining. (R. Agrawal, P. Stolorz, G. Piatetsky-Shapiro, eds.), pp. 101104. T.S. Richardson (1997). Extensions of Undirected and Acyclic, Directed Graphical Models. In Proceedings of Artificial Intelligence and Statistics ’97, (D. Madigan and P. Smyth, eds.), pp.407-419. T.S. Richardson, P.Spirtes, C.Glymour (1997). A Note on Cyclic Graphs and Dynamical Feedback Systems. In Proceedings of Artificial Intelligence and Statistics ‘97, (D. Madigan and P. Smyth eds.), pp.421428. P.Spirtes, T.S. Richardson (1997). A Polynomial Time Algorithm for Determining DAG Equivalence in the Presence of Latent Variables and Selection Bias. In Proceedings of Artificial Intelligence and Statistics ‘97, (D. Madigan and P. Smyth eds.), pp.489-500.

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P.Spirtes, T.S. Richardson, C.Meek (1997). Heuristic Greedy Search Algorithms for Latent Variable Models. In Proceedings of Artificial Intelligence and Statistics ‘97, (D. Madigan and P. Smyth eds.), pp.481488. T.S. Richardson (1996). A Polynomial-Time Algorithm for Deciding Markov Equivalence of Directed Cyclic Graphical Models. In Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence, Portland, Oregon. E.Horvitz and F.Jensen (eds.), Morgan Kaufmann, San Francisco, CA, pp.462- 469. T.S. Richardson (1996). A Discovery Algorithm for Directed Cyclic Graphs. In Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence, Portland, Oregon, 1996. (E. Horvitz and F. Jensen eds.), Morgan Kaufmann, San Francisco, CA pp.454- 461. P.Spirtes, C.Meek, and T.S. Richardson (1995). Causal Inference in the Presence of Latent Variables and Selection Bias. Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp. 482-487. T.S. Richardson (1994). Equivalence in Non-Recursive Structural Equation Models. In Proceedings of The 11th Symposium on Computational Statistics, COMPSTAT, 20-26 August 1994, Vienna, Austria. (R.Dutter ed.), Physica Verlag, Vienna, pp.482-487. OTHER E. Moodie, T.S. Richardson (2005). A new variance for recursive gCONFERENCE estimation of optimal dynamic treatment regimes. Proceedings, WNAR PAPERS 2005. A. Ali, T. Richardson (2004) Searching across Markov equivalence classes of maximal ancestral graphs. Proceedings, JSM 2004. T.S. Richardson (1999). A Local Markov Property for Acyclic Directed Mixed Graphs. Proceedings, ISI Conference, Helsinki 1999, 4 pp.

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T.S. Richardson, 1999, 4 pp.

H.Bailer, M. Banerjee (1999). Specification

Searches Using MAG Models. Proceedings, ISI Conference, Helsinki

REFEREED T.S. Richardson and P.Spirtes (2003). Causal Inference via ancestral BOOK graph Markov models (with discussion). In Highly Structured Stochastic CHAPTERS Systems, edited by Peter Green, Nils Hjort and Sylvia Richardson to be published by Oxford University Press pp.83-105. T.S. Richardson (1998). Chain Graphs and Symmetric Associations. In Learning in Graphical Models, (M.Jordan ed.), Kluwer, (republished, 1999, MIT Press), pp.231-259. S. Andersson, D. Madigan, M. Perlman, and T.S. Richardson (1999). Graphical Markov Models in Multivariate Analysis. In Multivariate Analysis, Design of Experiments, and Survey Sampling, (S. Ghosh ed.), Marcel Dekker. OTHER BOOK T.S. Richardson, L.Schulz, A.Gopnik (2007) Data-mining probabilists CHAPTERS or experimental determinists? : A Dialogue on the Principles underlying Causal Learning in Children. In Causal Learning: Psychology, Philosophy and Computation (A.Gopnik, L.Schulz eds.) Oxford: Oxford University Press. T.S. Richardson, P.Spirtes (1999). Automated discovery of linear feedback models. In Computation, Causation and (C.Glymour and G.Cooper eds.), MIT Press, pp.253-302. Discovery,

R. Scheines, C. Glymour, P. Spirtes, C. Meek and T.S. Richardson (1999). Truth is among the best explanations: Finding causal explanations of conditional independence and dependence. In Computation, Causation and Discovery, (C. Glymour and G. Cooper eds.), MIT Press, pp.167-209.

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P.Spirtes, C. Meek, T.S. Richardson (1999). An algorithm for causal inference in the presence of latent variables and selection bias. In Computation, Causation and Discovery (C.Glymour and G.Cooper eds.), MIT Press, pp.211-252. DISCUSSIONS J. Robins, T.J. vanderWeele, T.S. Richardson (2007). Comment on Causal effects in the presence of non compliance a latent variable interpretation by A. Forcina. Metron LXIV (3) pp.288-298. T.S. Richardson (2004) Contribution to discussion of paper on Ecological Inference by J. Wakefield. Journal of the Royal Statistical Society, 167(3) Ser A. C. Glymour, P. Spirtes and T.S. Richardson (1999). On the possibility of inferring causation from association without background knowledge. A response to a paper by J. Robins and L. Wasserman, and reply to a rejoinder. In Computation, Causation and Discovery, (C.Glymour and G.Cooper eds.), MIT Press, pp.323-332, pp.343-345. T.S. Richardson (1999). Discussion of Computationally Efficient Methods for Selecting Among Mixtures of Graphical Models, by B. Thiesson, M. Chickering, D. Heckerman, and C. Meek. Bayesian Statistics 6, to appear 1999. G. Ridgeway, T.S. Richardson, and D. Madigan (1999). Discussion of Bump Hunting in High-Dimensional Data by J. Friedman and N. Fisher. Statistics and Computing, 9(2), pp.150-152. BOOK T.S. Richardson (1997). Review of An Introduction to Bayesian REVIEW Networks by F.V. Jensen. Journal of the American Statistical Association, 92 (439) pp.1215-1216. BOOKS T. Jaakkola, T.S. Richardson (2001) Proceedings of the Eighth EDITED International Conference on Artificial Intelligence and Statistics. Morgan Kaufmann.

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R. Dechter, T.S. Richardson (2006) Proceedings of the Twenty-Second Conference on Uncertainty and Artificial Intelligence. AUAI Press. EDITORIAL T.S. Richardson (2000). Prediction and Model Selection. Statistics and Computing. TECHNICAL S. Chaudhuri, T.S. Richardson , J. Robins, and E. Zivot (2007). SplitREPORTS & Sample Score Tests in Linear Instrumental Variables Regression. CSSS SUBMITTED Working paper no.73. Submitted to Econometric Theory. PAPERS E. Moodie, T.S. Richardson (2007). Bias Correction in NonDifferentiable Estimating Equations for Optimal Dynamic Regimes. COBRA Preprint Series. Article 17. Submitted to Scandinavian Journal of Statistics. M. Miyamura, T.S. Richardson (2006). Bi-partial covariances and Gaussian ancestral graph models. Submitted to Probability Theory and Related Fields. M. Drton, M. Eichler, T.S. Richardson (2006). Identification and likelihood inference for recursive linear models with correlated errors. arXiv:math.ST/0601631. Submitted to JASA. T.S. Richardson (2006) A factorization criterion for ancestral graphs. Work in progress. M. Drton, T.S. Richardson (2004). Graphical Answers to Questions About Likelihood Inference in Gaussian Covariance Models. Department of Statistics, University of Washington, Tech. Report 467. D. Heckerman, C. Meek, T.S. Richardson (2004) Variations on undirected graphical models and their relationships. Unpublished Technical Report.

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A. Ali, T.S. Richardson and P. Spirtes (2004) Markov Equivalence for Ancestral Graphs. Department of Statistics, University of Washington, Tech. Report 464. J. A. Wegelin, T.S. Richardson and D. L. Ragozin (2001). Rank-One Latent Models for Cross-Covariance. UW Department of Statistics, Technical Report, No. 391, 29 pp. T.S. Richardson (2001) Chain graphs which are maximal ancestral graphs are recursive causal graphs. UW Department of Statistics, Technical Report, No. 387, 13 pp. N. Wermuth, D.R. Cox, T.S. Richardson and G. Glonek (1999). On transforming and generating cyclic graph models. ZUMA Technical Report. 12 pp. T.S. Richardson (1996). Fast re-calculation of the covariance matrix implied by a recursive structural equation model. Technical Report, CMU-PHIL-67, 9 pp. P.Spirtes, T.S. Richardson, C.Meek, R.Scheines and C.Glymour (1996). Using d-separation to calculate zero partial correlations in linear models with correlated errors., Technical Report, CMU-PHIL-72. 17pp.

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- 贝叶斯模型的属性数据(Bayesian Models for Categorical Data)
- A Divisive Ordering Algorithm for Mapping Categorical Data to Numeric Data
- SUBSPACE CLUSTERING FOR HIGH DIMENSIONAL CATEGORICAL DATA
- A fuzzy k-modes algorithm for clustering categorical data
- Ignorability for categorical data
- Chapter 1 A Guide through Latent Structure Models for Categorical Data
- HE Plots for Multivariate Linear Models
- A Categorical Framework for Conceptual Data Modeling Definition, Application, and Implement
- An algorithm for clustering data__ streams of categorical attributes

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1.3 Log Linear*Models* Many problems in *categorical* *data* analysis can be ...(1990). Graphical *Models* in Applied *Multivariate* Statistics. Chichester: Wiley... 更多相关标签：

Bayesian semiparametric regression analysis of multi

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ank) of these attributes to be nite sets 1 of

Applied

Linear

dichotomous or ordered

densities fk (x; ?k ) are taken to be

cance of A or C:

In this paper we present a group of

(z ,z ,…, )T is 2.2 The irfst.order

tables and graphical Gaussian

Tree Classifier Using

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贝叶斯模型的属性数据(Bayesian

1.3 Log Linear