Computerized Adaptive Testing: May 2005

Tuesday, May 31, 2005

My current job

Recently, I am preparing my master thesis with title of Computerized Adaptive Testing Based on Bayesian Decision Theory: Uni- and Multidimensional. It is a conclusion about my gain during one year of master degree study in the promising engineering school affiliated to University of Montreal: Ecole Polytechnique de Montreal. Wish it will be satisfactory by myself. Show my acknowledgement to my supervisor Michel Desmarais and my wife Huang Shan.

My next plan

I experienced a failure of transferrring from computer science to educational measurement program for Ph.D directly. However, I find that I can still contribute something from the aspect of educational technology, so finally I decide to continue my Ph.D of computer science with research major of user modeling and AI in education. Thanks for my supervisor Michel Desmarais for accepting me as his Ph.D student, and we are looking forward to more cooperation and progress in this promising field.

Monday, May 30, 2005

References for learning Bayesian Networks and more general graph models

Books

In reverse chronological order (bold means particularly recommended)

F. V. Jensen. "Bayesian Networks and Decision Graphs". Springer. 2001.
Probably the best introductory book available.
D. Edwards. "Introduction to Graphical Modelling", 2nd ed. Springer-Verlag. 2000.
Good treatment of undirected graphical models from a statistical perspective.
J. Pearl. "Causality". Cambridge. 2000.
The definitive book on using causal DAG modeling.
R. G. Cowell, A. P. Dawid, S. L. Lauritzen and D. J. Spiegelhalter. "Probabilistic Networks and Expert Systems". Springer-Verlag. 1999.
Probably the best book available, although the treatment is restricted to exact inference.
M. I. Jordan (ed). "Learning in Graphical Models". MIT Press. 1998.
Loose collection of papers on machine learning, many related to graphical models. One of the few books to discuss approximate inference.
B. Frey. "Graphical models for machine learning and digital communication", MIT Press. 1998.
Discusses pattern recognition and turbocodes using (directed) graphical models.
E. Castillo and J. M. Gutierrez and A. S. Hadi. "Expert systems and probabilistic network models". Springer-Verlag, 1997.
A Spanish version is available online for free.
F. Jensen. "An introduction to Bayesian Networks". UCL Press. 1996. Out of print.
Superceded by his 2001 book.
S. Lauritzen. "Graphical Models", Oxford. 1996.
The definitive mathematical exposition of the theory of graphical models.
S. Russell and P. Norvig. "Artificial Intelligence: A Modern Approach". Prentice Hall. 1995.
Popular undergraduate textbook that includes a readable chapter on directed graphical models.
J. Whittaker. "Graphical Models in Applied Multivariate Statistics", Wiley. 1990.
This is the first book published on graphical modelling from a statistics perspective.
R. Neapoliton. "Probabilistic Reasoning in Expert Systems". John Wiley & Sons. 1990.
J. Pearl. "Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference." Morgan Kaufmann. 1988.
The book that got it all started! A very insightful book, still relevant today.

Review articles

P. Smyth, 1998. "Belief networks, hidden Markov models, and Markov random fields: a unifying view", Pattern Recognition Letters.
E. Charniak, 1991. "Bayesian Networks without Tears", AI magazine.
Sam Roweis & Zoubin Ghahramani, 1999. A Unifying Review of Linear Gaussian Models, Neural Computation 11(2) (1999) pp.305-345

Exact Inference

C. Huang and A. Darwiche, 1996. "Inference in Belief Networks: A procedural guide", Intl. J. Approximate Reasoning, 15(3):225-263.
R. McEliece and S. M. Aji, 2000. The Generalized Distributive Law, IEEE Trans. Inform. Theory, vol. 46, no. 2 (March 2000), pp. 325--343.
F. Kschischang, B. Frey and H. Loeliger, 2001. Factor graphs and the sum product algorithm, IEEE Transactions on Information Theory, February, 2001.
M. Peot and R. Shachter, 1991. "Fusion and propogation with multiple observations in belief networks", Artificial Intelligence, 48:299-318.

Approximate Inference

M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul, 1997. "An introduction to variational methods for graphical models."
D. MacKay, 1998. "An introduction to Monte Carlo methods".
T. Jaakkola and M. Jordan, 1998. "Variational probabilistic inference and the QMR-DT database"

Learning

W. L. Buntine, 1994. "Operations for Learning with Graphical Models", J. AI Research, 159--225.
D. Heckerman, 1996. "A tutorial on learning with Bayesian networks", Microsoft Research tech. report, MSR-TR-95-06.

DBNs

L. R. Rabiner, 1989. "A Tutorial in Hidden Markov Models and Selected Applications in Speech Recognition", Proc. of the IEEE, 77(2):257--286.
Z. Ghahramani, 1998. Learning Dynamic Bayesian Networks In C.L. Giles and M. Gori (eds.), Adaptive Processing of Sequences and Data Structures . Lecture Notes in Artificial Intelligence, 168-197. Berlin: Springer-Verlag.

Saturday, May 21, 2005

Embedded CAT into study guide

CAT, for a long time, works indepedently as a testing system. We are satisfactory with its performance. However, with the higher requirement of education technology, people are searching for more intelligent tutorial tools for students. Study Guide is such a trend since it provides a more comprehensive solution. To implement adaptive Study Guide system, CAT is a necessary part to determine which step to go nextly for specific student. The related adaptive idea of CAT can be applied in Study Guide too, including selection rule, stopping rule, and decision rule. Anyway, it is a promising direction waiting our further effort.

Sunday, May 15, 2005

Hi, welcome to Shunkai's Fu personal bog all alone. Your comments are highly appreicated here. Wish you enjoy it!

Tuesday, May 10, 2005

CAT Based on Bayesian Networks

Bayesian Networks (BNs) is a promising graphical tool for causal inference. Some trials of applying it to CAT have been made, including Dr. Michel Desmarais, my supervisor. It seems an interesting topic, so I will put some time on it in the recent future to see if something useful be found. My focus is to check if it is easy to set up a net reflecting the relation between decision states and items.

It is a personal BLOG on CAT but open to everyone who is interested on this field or want to know something happen behind popular computerized adaptive testing such as GRE, TOEFL, SAT etc. Author of this BLOG is a master candidate of Ecole Polytechnique de Montreal, one affiliated engineering school to University of Montreal(www.umontreal.ca).