CSCE 790 Section 1 Spring 2019 Syllabus

CSCE 790 Section 1 Spring 2019: Syllabus

There is no textbook for this course. There will be assigned readings. The following five books should be on the shelf of every researcher working in the field of Probabilistic Graphical Models (PGMs).

Jensen, Finn V. and Thomas D. Nielsen. Bayesian Networks and Decision Graphs. New York: Springer-Verlag, 2007 (ISBN-10 0-387-68281-3; ISBN-13: 978-0-387-68281-5). (This text is referred to as [J01].)
Steffen L. Lauritzen. Graphical Models. Oxford: Clarendon Press, 1996 (ISBN: 0198-52219-3; ISBN 13: 978-0198-52219-5). (This text is referred to as [L96].)
Richard E. Neapolitan. Learning Bayesian Networks. Pearson, 2003 (ISBN-10: 0130125342; ISBN-13: 978-0130125347.) (This text is referred to as [N04].)
Judea Pearl. Probabilistic Reasoning in Intelligent Systems. San Francisco: Morgan-Kaufmann, 1988 (ISBN 0-934613-73-7), (This text is referred to as [P88].)
Judea Pearl. Causality, 2nd Edition. Cambridge University Press, 2009 (ISBN: 0521-89560-X; ISBN 13: 978-0521-89560-6). (This text is referred to as [P09].)

Other recommended books on the topic are:

Adnan Darwiche. Modeling and Reasoning with Bayesian Networks Cambridge, UK: Cambridge University Press, 2009 (ISBN 978-0-521-88438-9). (This text is referred to as [D09].)
Daphne Koller and Nir Friedman. Probabilistic Graphical Models: Principles and Techniques. Cambridge, MA: The MIT Press, 2009 (ISBN 978-0-262-0139-2).
Timo Koski and John M. Noble. Bayesian Networks: An Introduction. Chichester, UK: Wiley, 2009 (ISBN 978-0-470-74304-1).
Uffe B. Kjaerulff and Anders L. Madsen. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis. New York: Springer-Verlag, 2008 (ISBN 978-0-387-74100-0).
Kevin B. Korb and Ann E. Nicholson. Bayesian Artificial Intelligence, 2nd ed. London: Chapman & Hall and CRC Press, 2011 (ISBN 978-1-4398-1591-5). (First edition: London: Chapman & Hall and CRC Press, 2004, ISBN 1-58488-387-1.)
Robert G. Cowell, A. Philip Dawid. Steffen L. Lauritzen, and David J. Spiegelhalter. Probabilistic Networks and Expert Systems. New York: Springer, 1999 (ISBN 0-387-98767-3).
Jensen, Finn V. An Introduction to Bayesian Networks. New York: Springer-Verlag, 1996 (ISBN 0-387-91502-8). (This text is referred to as [J96].)
Richard E. Neapolitan. Probabilistic Reasoning in Expert Systems: Theory and Algorithms. New York: Wiley, 1990 (ISBN 0-471-61840-3).
Peter Spirtes, Clark Glymour, and Richard Scheines. Causation, Prediction, and Search, 2nd ed. Cambrdige, MA: MIT Press, 2000 (ISBN-10: 0262527928; ISBN-13: 978-0262527927).

The instructional delivery strategy for this course is a mix of lectures and student presentations of papers from the literature with discussion led and moderated by the instructor. The first day of classes is Tuesday, January 15, 2019. The last day to drop the course without a grade of "W" being recorded is Tuesday, January 22, 2019. The last day to withdraw without failure is Monday, March 4, 2019. The last day of classes is Thursday, April 25, 2019. There will be no midterm exam. The final exam will take place at the regularly scheduled time for courses taught from 1140 to 1255 on Tuesdays and Thursdays.

The assigned readings may be adjusted by the instructor according to the background and the interests of the students. The papers assigned in the last several weeks will be determined by the instructor based on the background and the interests of the students.

Week	Lecture or Presentation Topics	Readings
1: January 15 and 17	Probability Update: The Basics (Lectures)	Instructor's Notes.
2: January 22 and 24	Probability Update: Variable Elimination and Valuation-Based Systems (Presentations)	Rina Dechter. "Probabilistic Networks." Chapter 14 in Constraint Processing. San Francisco: Morgan Kaufmann, 2003. Shenoy, Prakash P. and Shafer, G. "Axioms for Probability and Belief-Function Propagation." In J. Pearl and G. Shafer, editors, Readings in Uncertain Reasoning, pp. 575-610. San Mateo, CA: Morgan Kaufmann, 1990. Stefano Bistarelli, Ugo Montanari, and Francesca Rossi. "Semiring-Based Constraint Satisfaction and Optimization," Journal of the ACM 44, 2 (March 1997), pp.201-236. Steffen L. Lauritzen and Finn V. Jensen, "Local Computations with Valuations from a Commutative Semigroup," Annals of Mathematics and Artificial Intelligence, 21 (1997), pp.51-69
3: January 29 and 31	Probability Update: The Junction Tree Algorithm (Presentations)	The Junction Tree Algorithms. Chapter 4 and Appendix 2 of [J96]. Finn Verner Jensen and Frank Jensen. "Optimal Junction Trees." Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI-1994), pp.360-366.
4: February 5 and 7	Probability Update: Exploiting Evidence with Factor Trees and Lazy Propagation; Inference by Conditioning (Presentations)	Mark Bloemeke and Marco Valtorta. "A Hybrid Algorithm to Compute Marginal and Joint Beliefs in Bayesian Networks and Its Complexity." In: G.F. Cooper and S. Moral (eds.), Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference. San Francisco, CA: Morgan-Kaufmann, 1998, pp.16-23. Anders L. Madsen and Finn Verner Jensen. "Lazy Propagation in Junction Trees." In: G.F. Cooper and S. Moral (eds.), Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference. San Francisco, CA: Morgan-Kaufmann, 1998, pp.362-369. Anders Madsen, Cory Butz, Jhonathan Oliveira, and Andre Dos Santos. "Simple Propagation with Arc Reversal in Bayesian Networks." Proceedings of the 9th International Conference on Probabilistic Graphical Models (PGM-18), PMLR-72 (Vaclav Kratochvil, Milan Studeny, editors), 2018, pp.260-271. Adnan Darwiche. "Inference by Conditioning." Chapter 8 (pp.178-201) in [D09].
5: February 12 and 14	The Graphoid Axioms and Markov Properties for Markov Networks, Bayesian Networks, and Chain Graphs (Lectures)	Chapter 3 [L96]
6: February 19 and 21	Some Advanced PGMs (Presentations)	Thomas Richardson and Peter Spirtes. "Ancestral Graph Markov Models." The Annals of Statistics, 30, 4, 962-1030, 2002. Mohammad Ali Javidian and Marco Valtorta. "On the Properties of MVR Chain Graphs." Workshop at the International Conference on Probabilistic Graphical Model (PGM-18), pp.13-24 (Vaclav Kratochvil and Milan Studeny, editors). Prague, Czech Republic, September 11-14, 2018. (Locally published proceedings; available at http://pgm2018.utia.cz/data/workshopproceedings.pdf; long version of the paper at ArXiv.org.)
7: February 26 and 28	Causal Models: Causal Bayesian Networks (Lectures)	Chapter 3 [P09]. Judea Pearl. "Causal Diagrams for Empirical Research." Biometrika, 82, 4, 669-710, 1995. Marco Valtorta and Yimin Huang. "Identifiability in Causal Bayesian Networks: A Gentle Introduction." Cybernetics and Systems, 39, 4, 425-442, 2008.
8: March 5 and 7	Causal Models: Properties and Limitations of the Do-Calculus (Presentations)	Yimin Huang and Marco Valtorta. "On the Completeness of an Identifiability Algorithm for Semi-Markovian Models." Annals of Mathematics and Artificial Intelligence, 54, 4, pp.363-408, 2009. Yimin Huang and Marco Valtorta. "Pearl's Calculus of Intervention is Complete." Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence (UAI-06), Cambridge, MA, July 13-16, 2006, pp.437-444. Elizabeth S. Allman, John A. Rhodes, Elena Stanghellini, and Marco Valtorta. "Parameter Identifiability of Discrete Bayesian Networks with Hidden Variables." Journal of Causal Inference, 3, 2, pp.189-205, 2015 (referred to as [ARSV]).
9: March 12 and 14	Spring Break	No Classes
10: March 19 and 21	Structure Learning: Constraint-Based, Score-Based, and Hybrid (Lectures)	Chapter 10 [N04] Moninder Singh and Marco Valtorta. "Construction of Bayesian Belief Networks from Data: a Brief Survey and an Efficient Algorithm." International Journal of Approximate Reasoning, 12, 2 (February 1995), 111-131.
11: March 26, 28	Structure Learning (Presentations)	Diego Colombo and Marloes H. Maathuis. "Order-Independent Constraint-Based Causal Structure Learning." Journal of Machine Learning Research, 15 (2014), pp.3921-3962. Xianchao Xie, Zhi Geng, and Qiang Zhao. "Decomposition of structural learning about directed acyclic graphs." Artificial Intelligence, 170 (2006), pp.422-439. Marco Scutari, Catharina E. Graafland, and Jose Manuel Gutierrez. "Who Learns Better Bayesian Network Structures: Constraint-Based, Score-Based, or Hybrid Algorithms?" Proceedings of the 9th International Conference on Probabilistic Graphical Models (PGM-18), PMLR-72 (Vaclav Kratochvil, Milan Studeny, editors), 2018, pp.416-427.
12: April 2 and 4	Some R Packages (Presentations)	pcalg, https://CRAN.R-project.org/package=pcalg. Markus Kalisch, Martin Maechler, Diego Colombo, Marloes H. Maathuis, and Peter Buehlmann. "Causal Inference Using Graphical Models with the R Package pcalg." Journal of Statistical Software, 47(11), 1-26, 2012, URL http://www.jstatsoft.org/v47/i11/. bnlearn, https://CRAN.R-project.org/package=bnlearn. Marco Scutari. "Learning Bayesian Networks with the bnlearn R Package." Journal of Statistical Software, 35(3), 1-22, 2010, URL http://www.jstatsoft.org/v35/i03/. causaleffect, https://CRAN.R-project.org/package=causaleffect. Santtu Tikka, and Juha Karvanen. "Identifying Causal Effects with the R Package causaleffect." Journal of Statistical Software, 76(12), 1-30, 2017.
13: April 9 and 11	Soft (Probabilistic) Evidence	Marco Valtorta, Jiri Vomlel, and Young-Gyun Kim. "Soft Evidential Update for Multiagent Systems." International Journal of Approximate Reasoning, 29, 1 (January 2002), 71-106, 2002. Young-Gyun Kim, Marco Valtorta, and Jiri Vomlel. "A Prototypical System for Soft Evidential Update." Applied Intelligence, 21, 1 (July-August 2004), 81-97, 2004. Scott Langevin and Marco Valtorta. "Performance Evaluation of Algorithms for Soft Evidential Update in Bayesian Networks: First Results." Proceedings of the Second International Conference on Scalable Uncertainty Management (SUM-08), Naples, Italy, October 1-3, 2008, pp. 284-297 Ali Ben Mrad1, Veronique Delcroix, Sylvain Piechowiak, Philip Leicester, and Mohamed Abid. "An explication of uncertain evidence in Bayesian networks: likelihood evidence and probabilistic evidence." Applied Intelligence, Volume 43, Issue 4, December 2015, Pages 802-824.
14: April 16 and 18	TBD	TBD
15: April 23 and 25	TBD	TBD