scholarly journals Distribution of Bactrocera oleae (Rossi, 1790) throughout the Iberian Peninsula based on a maximum entropy modelling approach

2020 ◽  
Vol 177 (1) ◽  
pp. 112-120 ◽  
Author(s):  
Jacinto Benhadi‐Marín ◽  
Sónia A.P. Santos ◽  
Paula Baptista ◽  
José Alberto Pereira
Keyword(s):  
Iberian Peninsula ◽  
Maximum Entropy ◽  
Bactrocera Oleae ◽  
Maximum Entropy Modelling ◽  
Modelling Approach

The Potential Effects of Climate Change on Suitable Habitat for the Sichuan Hill Partridge (Arborophila rufipectus,Boulton): Based on the Maximum Entropy Modelling

2014 ◽  
Vol 62 (4) ◽  
pp. 771-787 ◽  
Author(s):  
Juncheng Lei ◽  
Haigen Xu ◽  
Peng Cui ◽  
Qingwei Guang ◽  
Hui Ding
Keyword(s):  
Climate Change ◽  
Maximum Entropy ◽  
Suitable Habitat ◽  
Maximum Entropy Modelling

Maximum Entropy Modelling for Assessing Results on Real-Valued Data

2011 ◽  
Author(s):  
Kleanthis-Nikolaos Kontonasios ◽  
Jilles Vreeken ◽  
Tijl De Bie
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Modelling

Statistical Methods

2012 ◽  
Author(s):  
Christer Samuelsson
Keyword(s):  
Language Learning ◽  
Maximum Entropy ◽  
Statistical Methods ◽  
Language Processing ◽  
Mathematical Statistics ◽  
Semantic Interpretation ◽  
Entropy Model ◽  
Heterogeneous Information ◽  
Part Of Speech ◽  
Maximum Entropy Modelling

Statistical methods now belong to mainstream natural language processing. They have been successfully applied to virtually all tasks within language processing and neighbouring fields, including part-of-speech tagging, syntactic parsing, semantic interpretation, lexical acquisition, machine translation, information retrieval, and information extraction and language learning. This article reviews mathematical statistics and applies it to language modelling problems, leading up to the hidden Markov model and maximum entropy model. The real strength of maximum-entropy modelling lies in combining evidence from several rules, each one of which alone might not be conclusive, but which taken together dramatically affect the probability. Maximum-entropy modelling allows combining heterogeneous information sources to produce a uniform probabilistic model where each piece of information is formulated as a feature. The key ideas of mathematical statistics are simple and intuitive, but tend to be buried in a sea of mathematical technicalities. Finally, the article provides mathematical detail related to the topic of discussion.

Maximum Entropy Modelling of Mixture Distributions

Kybernetes ◽  
1994 ◽  
Vol 23 (1) ◽  
pp. 49-54 ◽  
Author(s):  
Dimitris N. Politis
Keyword(s):  
Maximum Entropy ◽  
Mixture Distributions ◽  
Maximum Entropy Modelling

Inferring climatic controls of rice stem borers’ spatial distributions using maximum entropy modelling

2018 ◽  
Vol 142 (4) ◽  
pp. 388-396 ◽  
Author(s):  
M. Jalaeian ◽  
A. Golizadeh ◽  
A. Sarafrazi ◽  
B. Naimi
Keyword(s):  
Maximum Entropy ◽  
Spatial Distributions ◽  
Stem Borers ◽  
Rice Stem Borers ◽  
Maximum Entropy Modelling

scholarly journals Change of Potential Distribution Area of a Forest Tree Acer davidii in East Asia Under the Context of Climate Oscillations

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 689
Author(s):  
Zidong Su ◽  
Xiaojuan Huang ◽  
Qiuyi Zhong ◽  
Mili Liu ◽  
Xiaoyu Song ◽  
...  
Keyword(s):  
Southeast Asia ◽  
East Asia ◽  
South Asia ◽  
Maximum Entropy ◽  
Species Distribution ◽  
Forest Tree ◽  
Distribution Area ◽  
Climate Oscillations ◽  
Maximum Entropy Modelling ◽  
Suitable Area

The climate oscillations of the quaternary periods have profoundly affected the geographic distributions of current species. Acer davidii is a deciduous forest tree species mainly distributed in East Asia and China, playing a dominant role in the local forest ecosystem. In order to study the potential changes of geographic distributions of A. davidii in climate fluctuations, we collected the relate geographical distribution data and six climatic variables, using maximum entropy modelling to determine the species distribution. The results showed that the Areas Under Curve (AUC) values of the working characteristic curves of the subjects in the five historical periods were all greater than 0.93, suggesting that the results of maximum entropy modelling were accurate. The simulation of species distribution showed that the suitable area of A. davidii was mainly concentrated in central and northern China in contemporary times. From the Last Interglacial Age (LIG) to the Last Glacial Maximum (LGM), and then to the future (2050, 2070), the distribution area of this species experienced a decrease (LGM~Current; the high adaptability areas of central China became moderate) then an increase (Current~2050, the adaptation areas expanded to South Asia, Southeast Asia, and Siberia), and finally decreased (2050~2070, the suitable areas of South Asia, Southeast Asia, and Siberia shrank returning to China at latitude 25 °N). Compared to the LGM, the area of contemporary suitable area increased. Interestingly, the area of suitable growth range under future climatic conditions (2050) increased by half than before, and the suitable distribution area moved from Midwest China to Northeast China. This study on the change of species distribution can provide a typical case for the model study on the response of plants to climate change in the north temperate and subtropical zones of East Asia. Meanwhile, it can also give a basis for planting planning, species protection, and management.

scholarly journals A Maximum Entropy Modelling of the Rain Drop Size Distribution

Entropy ◽  
2011 ◽  
Vol 13 (2) ◽  
pp. 293-315 ◽  
Author(s):  
Ramiro Checa ◽  
Francisco J. Tapiador
Keyword(s):  
Maximum Entropy ◽  
Size Distribution ◽  
Drop Size ◽  
Drop Size Distribution ◽  
Rain Drop ◽  
Maximum Entropy Modelling
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