Maximum entropy electron density estimation

Maximum Entropy Density Estimation with Incomplete Data

We propose a natural generalization of Regularized Maximum Entropy Density Estimation (maxent) to handle input data with unknown values. While standard approaches to handling missing data usually involve estimating the actual unknown values, then using the estimated, complete data as input, our method avoids the two-step process and handles unknown values directly in the maximum entropy formula...

Consistency and Generalization Bounds for Maximum Entropy Density Estimation

We investigate the statistical properties of maximum entropy density estimation, both for the complete data case and the incomplete data case. We show that under certain assumptions, the generalization error can be bounded in terms of the complexity of the underlying feature functions. This allows us to establish the universal consistency of maximum entropy density estimation.

Stochastic Information Gradient Algorithm Based on Maximum Entropy Density Estimation

Correcting sample selection bias in maximum entropy density estimation

We study the problem of maximum entropy density estimation in the presence of known sample selection bias. We propose three bias correction approaches. The first one takes advantage of unbiased sufficient statistics which can be obtained from biased samples. The second one estimates the biased distribution and then factors the bias out. The third one approximates the second by only using sample...

Maximum Entropy Density Estimation with Incomplete Presence-Only Data

We demonstrate a generalization of Maximum Entropy Density Estimation that elegantly handles incomplete presence-only data. We provide a formulation that is able to learn from known values of incomplete data without having to learn imputed values, which may be inaccurate. This saves the effort needed to perform accurate imputation while observing the principle of maximum entropy throughout the ...