@inproceedings{74adee242c4f44a69f5cc12150dc817e,
title = "From data to constraints",
abstract = "Jaynes' Maximum Entropy (MaxEnt) inference starts with the assumption that we have a set of known constraints over the distribution. In statistical physics, we have a good intuition about the conserved macroscopic variables. It should not be surprising that in a real world applications, we have no idea about which coordinates to use for specifying the state of the system. In other words, we only observe empirical data and we have to take a decision on the constraints from the data. In an effort to circumvent this limitation, we propose a nonparametric quantile based method to extract relevant and significant facts (sufficient statistics) for the maximum entropy exponential model.",
keywords = "exponential model, Maximum entropy, mid-rank transformations, nonparametric Entropy estimation, quantile function",
author = "S. Mukhopadhyay and E. Parzen and Lahiri, \{S. N.\}",
year = "2012",
doi = "10.1063/1.3703617",
language = "English",
isbn = "9780735410398",
series = "AIP Conference Proceedings",
pages = "32--39",
booktitle = "Bayesian Inference and Maximum Entropy Methods in Science and Engineering - 31st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2011",
note = "31st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2011 ; Conference date: 09-07-2011 Through 16-07-2011",
}