In this paper, the continuous optimal control theory is used to model and solve the maximum entropy problem for a continuous random variable. The maximum entropy principle provides a method to obtain least-biased probability density function (Pdf) estimation. In this paper, to find a closed form solution for the maximum entropy problem with any number of moment constraints, the entropy is consi...

The language models used for automatic speech recognition (ASR) are often based on very simple Markov models. This paper presents an overview of a more powerful modelling technique, Maximum Entropy (ME), and its application in langauge modelling. Preliminary results indicate that ME models are viable for this task, and perform slightly better than the traditional models.

One of the applications of the maximum-entropy method (MEM) in crystallography is the reconstruction of the electron density from phased structure factors. Here the application of the MEM to incommensurately modulated crystals and incommensurate composite crystals is considered. The MEM is computed directly in superspace, where the electron density in the (3+d)-dimensional unit cell (d > 0) is ...

Standard ensemble or particle filtering schemes do not properly represent states of low priori probability when the number of available samples is too small, as is often the case in practical applications. We introduce here a set of parametric resampling methods to solve this problem. Motivated by a general H-theorem for relative entropy, we construct parametric models for the filter distributi...

This paper proposes a method of estimating and simulating a maximum entropy distribution given moment conditions based on MonteCarlo approach. The method provides an simple alternative to conventional calculation methods of maximum entropy densities which involve complex numerical integration and are subject to occasional failure. We first show that maximum entropy density on a random sample co...