The research addressed here concerns the construction of the probability distribution of a random vector in high dimension using the maximum entropy (MaxEnt) principle under constraints defined by the available information. In this paper, a new algorithm, adapted to the high stochastic dimension, is proposed to identify the Lagrange multipliers introduced to take into account the constraints in...

Distribution data on threatened and endangered species are often sparse and clustered making it difficult to model their suitable habitat distribution using commonly used modeling approaches. We used a novel method called maximum entropy distribution modeling or Maxent for predicting potential suitable habitat for Canacomyrica monticola, a threatened and endangered tree species in New Caledonia...

This paper introduces a method to assess lexical stress patterns in American English words automatically using machine learning algorithms, which could be used on the computer assisted language learning (CALL) system. We aim to model human production concerning lexical stress patterns by training stress patterns in a native speaker’s utterances and making use of it to detect erroneous stress pa...

This paper revisits the problem of traffic generation from first principles: rather than following up on previous work of providing an explanatory model for why observed traffic matrices look the way they do, this paper generates TM instances subject to user specified spatial and temporal constraints. The authors hereby rely on the Maximum Entropy (MaxEnt) framework. Maximum Entropy is a powerf...

We present a theory, a system and an application for common sense reasoning based on propositional logic, the probability calculus and the concept of maximum entropy. The task of the system Pit (Probability Induction Tool) is to provide decisions under incomplete knowledge, while keeping the necessary additional assumptions as minimal and clear as possible. We therefore enrich the probability c...

This article addresses error detection in broadcast news automatic transcription, as a post-processing stage. Based on the observation that many errors appear in bursts, we investigated the use of Markov Chains (MC) for their temporal modelling capabilities. Experiments were conducted on a large Amercian English broadcast news corpus from NIST. Common features in error detection were used, all ...

Multiscale computational methods bridging models at micro-scale with macro-properties is a problem of practical significance. Probability distribution functions (PDFs) providing a complete representation of microstructural variability in 3D polycrystalline materials using limited information is difficult to obtain since this inverse problem is highly ill-posed. We use the maximum entropy (MaxEn...

Understanding temporal urban growth is very crucial to the interpretation of urban morphology and a key challenge for the study of rapid urbanization in contemporary China. Previous land use change modelling approaches only consider the spatial complexity that may be indicated by spatial dependence and landscape fragmentation, ignoring the temporal complexity inherent in the process of urban gr...