The Method of Maximum (relative) Entropy (ME) has been designed for updating from a prior distribution to a posterior distribution when the information being processed is in the form of a constraint on the family of allowed posteriors. This is in contrast with the usual MaxEnt which was designed as a method to assign, and not to update, probabilities. The objective of this paper is to strengthe...

We have demonstrated that the Maximum Entropy (ME) principle in the context of Bayesian probability theory can be used to derive the probability distributions of those processes characterized by its scaling properties including multiscaling moments and geometric mean. We started from a proof-of-concept case of a power-law probability distribution, followed by the general case of multifractality...

Algebraic statistics is a recently evolving field, where one would treat statistical models as algebraic objects and thereby use tools from computational commutative algebra and algebraic geometry in the analysis and computation of statistical models. In this approach, calculation of parameters of statistical models amounts to solving set of polynomial equations in several variables, for which ...

In our information era, keywords are very useful to information retrieval, text clustering and so on. News is always a domain attracting a large amount of attention. Aiming at news documents' characteristics and the resources available, this paper proposes to use Maximum Entropy (ME) model to conduct automatic keyword indexing. The focus of ME-based keyword indexing is how to obtain all the can...

A maximum entropy (ME) model is usually estimated so that it conforms to equality constraints on feature expectations. However, the equality constraint is inappropriate for sparse and therefore unreliable features. This study explores an ME model with box-type inequality constraints, where the equality can be violated to reflect this unreliability. We evaluate the inequality ME model using text...

We demonstrate how information in the form of observable data and moment constraints are introduced into the method of Maximum relative Entropy (ME). A general example of updating with data and moments is shown. A specific econometric example is solved in detail which can then be used as a template for real world problems. A numerical example is compared to a large deviation solution which illu...

Maximum Entropy (ME) deconvolution has become a widely used approach for image restoration. Although it has many attractive features, its computational burden is considerably larger than for competing linear inversion techniques. The present work accelerates ME restoration by using linear techniques within theME framework and proposes a simple and fast algorithmwhich is applicable to low-noise ...

Spelling check identifies incorrect writing words in documents. For the reason of input methods, Chinese spelling check is much different from English and it is still a challenging work. For the past decade years, most of the methods in detecting errors in documents are lexicon-based or probability-based, and much progress are made. In this paper, we propose a new method in Chinese spelling che...

In this paper, the speech separation task will be regarded as a convolutive mixture Blind Source Separation (BSS) problem. The Maximum Entropy (ME) algorithm, the Minimum Mutual Information (MMI) algorithm and the Maximum Likelihood (ML) algorithm are main approaches of the algorithms solving the BSS problem. The relationship of these three algorithms has been analyzed in this paper. Based on t...

While Maximum Entropy (ME) based learning procedures have been successfully applied to text based natural language processing, there are only little investigations on using ME for acoustic modeling in automatic speech recognition. In this paper we show that the well known Generalized Iterative Scaling (GIS) algorithm can be used as an alternative method to discriminatively train the parameters ...