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Probabilistic generative modelling

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2003) 2749 861-868
DOI: 10.1007/3-540-45103-x_114
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Abstract

The contribution of this paper is the adaption of data driven methods for decomposition of tangent shape variability proposed in a probabilistic framework. By Bayesian model selection we compare two generative model representations derived by principal components analysis and by maximum autocorrelation factors analysis. © Springer-Verlag 2003.

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Larsen, R., & Hilger, K. B. (2003). Probabilistic generative modelling. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2749, 861–868. https://doi.org/10.1007/3-540-45103-x_114

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