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A Framework for Probability Density Estimation
This video was recorded at NIPS Workshop on Representations and Inference on Probability Distributions, Whistler 2007. The talk introduces a new framework for learning probability density functions by assessing their performance against a set of 'test functions'. A theoretical analysis suggests that we can tailor a distribution for a class of tasks by training it to fit a small subsample. There is a trade-off between the complexity of the class of distributions used for the distribution and the complexity of the set of tasks. Experimental evidence is given to support the theoretical analysis.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
