Prosodic Boundary Prediction Based on Maximum Entropy Model with Error-Driven Modification

2006 ◽  
pp. 149-160 ◽  
Author(s):  
Xiaonan Zhang ◽  
Jun Xu ◽  
Lianhong Cai
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Prosodic Boundary

scholarly journals Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Arian Ashourvan ◽  
Preya Shah ◽  
Adam Pines ◽  
Shi Gu ◽  
Christopher W. Lynn ◽  
...  
Keyword(s):  
White Matter ◽  
Maximum Entropy ◽  
Large Scale ◽  
Structural Connectivity ◽  
Quantitative Relationship ◽  
Brain Regions ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Activation Patterns ◽  
Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

Video segmentation using Maximum Entropy Model

2005 ◽  
Vol 6 (S1) ◽  
pp. 47-52
Author(s):  
Li-juan Qin ◽  
Yue-ting Zhuang ◽  
Yun-he Pan ◽  
Fei Wu
Keyword(s):  
Maximum Entropy ◽  
Video Segmentation ◽  
Maximum Entropy Model ◽  
Entropy Model

Evaluation of factors affecting gully headcut location using summary statistics and the maximum entropy model: Golestan Province, NE Iran

2019 ◽  
Vol 677 ◽  
pp. 281-298 ◽  
Author(s):  
Narges Kariminejad ◽  
Mohsen Hosseinalizadeh ◽  
Hamid Reza Pourghasemi ◽  
Anita Bernatek-Jakiel ◽  
Giandiego Campetella ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Summary Statistics ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Factors Affecting ◽  
Golestan Province ◽  
Ne Iran

scholarly journals MPF–BML: a standalone GUI-based package for maximum entropy model inference

2019 ◽  
Vol 36 (7) ◽  
pp. 2278-2279
Author(s):  
Ahmed A Quadeer ◽  
Matthew R McKay ◽  
John P Barton ◽  
Raymond H Y Louie
Keyword(s):  
Maximum Entropy ◽  
Fitness Landscape ◽  
Sequence Data ◽  
Surface Proteins ◽  
Supplementary Information ◽  
Model Parameters ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Widespread Application ◽  
Maximum Entropy Models

Abstract Summary Learning underlying correlation patterns in data is a central problem across scientific fields. Maximum entropy models present an important class of statistical approaches for addressing this problem. However, accurately and efficiently inferring model parameters are a major challenge, particularly for modern high-dimensional applications such as in biology, for which the number of parameters is enormous. Previously, we developed a statistical method, minimum probability flow–Boltzmann Machine Learning (MPF–BML), for performing fast and accurate inference of maximum entropy model parameters, which was applied to genetic sequence data to estimate the fitness landscape for the surface proteins of human immunodeficiency virus and hepatitis C virus. To facilitate seamless use of MPF–BML and encourage more widespread application to data in diverse fields, we present a standalone cross-platform package of MPF–BML which features an easy-to-use graphical user interface. The package only requires the input data (protein sequence data or data of multiple configurations of a complex system with large number of variables) and returns the maximum entropy model parameters. Availability and implementation The MPF–BML software is publicly available under the MIT License at https://github.com/ahmedaq/MPF-BML-GUI. Supplementary information Supplementary data are available at Bioinformatics online.

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