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Convex Optimization

Convex Optimization Book Reviews 1097 their applications range from robotics to machine learning, from natural lan- clear. I even found their six technical appendixes to be clear and interesting. I can strongly recommend Procrustes Problems to those interested in Procrustes guage processing to image recognition, and along paths that are often com- pletely unrelated to graphical models. methods. F. James ROHLF Marco F. RAMONI Stony Brook University Harvard University REFERENCES REFERENCES Mardia, K. V., and Dryden, I. L. (1994), “Shape Averages and Their Bias,” Ad- Castillo, E., Gutierrez, J. M., and Hadi, A. S. (1997), Expert Systems and Prob- vances in Applied Probability, 26, 334–340. abilistic Network Models, New York: Springer-Verlag. Rohlf, F. J. (2003), “Bias and Error in Estimates of Mean Shape in Morphomet- Cowell, R. G., Dawid, A. P., Lauritzen, S. L., and Spiegelhalter, D. J. (1999), rics,” Journal of Human Evolution, 44, 665–683. Probabilistic Networks and Expert Systems, New York: Springer-Verlag. Lauritzen, S. L. (1996), Graphical Models, Oxford, U.K.: Clarendon Press. Pearl, J. (1990), Probabilistic Reasoning in Intelligent Systems,San Mateo, CA: Morgan Kaufmann. Thomas, A., Spiegelhalter, D. J., and Gilks, W. (1992), “BUGS: A Program to Perform Bayesian Inference Using Gibbs Sampling,” in Bayesian Statistics 4, eds. J. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the American Statistical Association Taylor & Francis

Convex Optimization

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References (205)

Publisher
Taylor & Francis
Copyright
© American Statistical Association
ISSN
1537-274X
eISSN
0162-1459
DOI
10.1198/jasa.2005.s41
Publisher site
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Abstract

Book Reviews 1097 their applications range from robotics to machine learning, from natural lan- clear. I even found their six technical appendixes to be clear and interesting. I can strongly recommend Procrustes Problems to those interested in Procrustes guage processing to image recognition, and along paths that are often com- pletely unrelated to graphical models. methods. F. James ROHLF Marco F. RAMONI Stony Brook University Harvard University REFERENCES REFERENCES Mardia, K. V., and Dryden, I. L. (1994), “Shape Averages and Their Bias,” Ad- Castillo, E., Gutierrez, J. M., and Hadi, A. S. (1997), Expert Systems and Prob- vances in Applied Probability, 26, 334–340. abilistic Network Models, New York: Springer-Verlag. Rohlf, F. J. (2003), “Bias and Error in Estimates of Mean Shape in Morphomet- Cowell, R. G., Dawid, A. P., Lauritzen, S. L., and Spiegelhalter, D. J. (1999), rics,” Journal of Human Evolution, 44, 665–683. Probabilistic Networks and Expert Systems, New York: Springer-Verlag. Lauritzen, S. L. (1996), Graphical Models, Oxford, U.K.: Clarendon Press. Pearl, J. (1990), Probabilistic Reasoning in Intelligent Systems,San Mateo, CA: Morgan Kaufmann. Thomas, A., Spiegelhalter, D. J., and Gilks, W. (1992), “BUGS: A Program to Perform Bayesian Inference Using Gibbs Sampling,” in Bayesian Statistics 4, eds. J.

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Journal of the American Statistical Association – Taylor & Francis

Published: Sep 1, 2005

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