Predicting the Spread of Acacia Nilotica Using Maximum Entropy Modeling
نویسندگان
چکیده
منابع مشابه
A Note on the Bivariate Maximum Entropy Modeling
Let X=(X1 ,X2 ) be a continuous random vector. Under the assumption that the marginal distributions of X1 and X2 are given, we develop models for vector X when there is partial information about the dependence structure between X1 and X2. The models which are obtained based on well-known Principle of Maximum Entropy are called the maximum entropy (ME) mo...
متن کاملModeling of Artemisia sieberi Besser Habitat Distribution Using Maximum Entropy Method in Desert Rangelands
Predictive modeling of habitat distribution of range plant species and identification of their potential habitats play important roles in the restoration of disturbed rangelands. This study aimed to predict the geographical distribution of Artemisia sieberi and find the influential variables in the distribution of A. sieberi in the desert rangelands of central Iran. Maps of environmental variab...
متن کاملMaximum Entropy Modeling Toolkit
The Maximum Entropy Modeling Toolkit supports parameter estimation and prediction for statistical language models in the maximum entropy framework. The maximum entropy framework provides a constructive method for obtaining the unique conditional distribution p*(y|x) that satisfies a set of linear constraints and maximizes the conditional entropy H(p|f) with respect to the empirical distribution...
متن کاملComparison of the in vitro anthelmintic effects of Acacia nilotica and Acacia raddiana
Gastrointestinal nematodes are a major threat to small ruminant rearing in the Sahel area, where farmers traditionally use bioactive plants to control these worms, including Acacia nilotica and Acacia raddiana. The main aim of this study was to screen the potential anthelmintic properties of aqueous and acetone extracts of leaves of these two plants based on three in vitro assays: (1) the egg h...
متن کاملPredicting Customer Behavior using Naive Bayes and Maximum Entropy
In this work we describe combinations of classifiers using Naive Bayes, Maximum Entropy, Neural Networks and Logistic Regression for classification of customer records. Performance of these approaches is confirmed by the 1st, 3rd, and 5th rank in the Data-Mining-Cup 2004.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: TELKOMNIKA (Telecommunication Computing Electronics and Control)
سال: 2018
ISSN: 2302-9293,1693-6930
DOI: 10.12928/telkomnika.v16i2.6894