Maximum-entropy (ME) inference of state probabilities using state-dependent constraints is popular in the study of complex systems. In stochastic systems, how state space topology and path-dependent constraints affect ME-inferred state probabilities remains unknown. To that end, we derive the transition probabilities and the stationary distribution of a maximum path entropy Markov process subje...

Conditional Random Fields (CRFs) are the state-of-the-art models for sequential labeling problems. A critical step is to select optimal feature template subset before employing CRFs, which is a tedious task. To improve the efficienc y of t his step, we propose a new method that adopts the maximum entropy (ME) model and maximum entropy Markov models (MEMMs) instead of CRFs considering the homolo...

This paper analyze Nash’s equilibrium (maximum utilility MU) and its relation with the order state (minimum entropy ME). I introduce the concept of minimum entropy as a paradigm of both Nash-Hayek’s equilibrium. The ME concept is related to Quantum Games. One question arises after completing this exercise: What do the Quantum Mechanics postulates indicate about Game Theory and Economics? Journa...

We model Mandarin phrase break prediction as a classification problem with three level prosodic structures and apply conditional maximum entropy classification to this problem. We acquire multiple levels of linguistic knowledge from an annotated corpus to become well-integrated features for maximum entropy framework. Five kinds of features were used to represent various linguistic constraints i...

In most applications of optical computed tomography (OpCT), limited-view problems are often encountered, which can be solved to a certain extent with typical OpCT reconstructive algorithms. The concept of entropy first emerged in information theory has been introduced into OpCT algorithms, such as maximum entropy (ME) algorithms and cross entropy (CE) algorithms, which have demonstrated their s...

Entropy is a measure for the uninformativeness or randomness of a data set, i.e., the higher the entropy is, the lower is the amount of information. In the field of propositional logic it has proven to constitute a suitable measure to be maximized when dealing with models of probabilistic propositional theories. More specifically, it was shown that the model of a probabilistic propositional the...

Concept of exponential family is generalized by simple and general exponential form. Simple and general potential are introduced. Maximum Entropy and Maximum Likelihood tasks are defined. ML task on the simple exponential form and ME task on the simple potentials are proved to be complementary in setup and identical in solutions. ML task on the general exponential form and ME task on the genera...

The objective of this article is two-fold. Firstly, a hybrid approach to Sentiment Analysis encompassing the use of Semantic Rules, Fuzzy Sets and an enriched Sentiment Lexicon, improved with the support of SentiWordNet is described. Secondly, the proposed hybrid method is compared against two well established Supervised Learning techniques, Näıve Bayes and Maximum Entropy. Using the well known...

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.

A necessary and suucient condition for the existence of the maximum entropy (ME) function deened in an innnite or semiinnnite interval, is provided. The conclusions reached show that, except in a few particular cases, the necessary and suucient conditions for the existence of maximum entropy function are identical to the conditions for the solution of the moment problem when the rst M+1 moments...