Investigation of the relationship between plant species and environmental factors plays an important role in plant ecology. The present study aimed to develop the best predicting model for distribution of Prangos uloptera DC. using logistic regression (LR) and Maximum Entropy Methods (MaxEnt) in its habitat in the southern rangeland of Ardabil province, Iran. Vegetation data (presence and absen...

We investigate Bayesian and Maximum Entropy methods for doing inference under uncertainty. This investigation is primarily through concrete examples that have been previously investigated in the literature. We find that it is possible to do Bayesian and MaxEnt inference using the same information, despite claims to the contrary, and that they lead to different results. We find that these differ...

Through completing an underspeciied probability model, Maximum Entropy (MaxEnt) supports non-monotonic inferences. Some major aspects of how this is done by MaxEnt can be understood from the background of two principles of rational decision: the concept of Indiierence and the concept of Independence. In a formal speciication MaxEnt can be viewed as (conservative) extension of these principles; ...

We compare the application of Bayesian inference and the maximum entropy (MaxEnt) method for the analysis of flow networks, such as water, electrical and transport networks. The two methods have the advantage of allowing a probabilistic prediction of flow rates and other variables, when there is insufficient information to obtain a deterministic solution, and also allow the effects of uncertain...

Maximum entropy inverse optimal control (MaxEnt IOC) is an effective means of discovering the underlying cost function of demonstrated agent’s activity. To enable inference in large state spaces, we introduce an approximate MaxEnt IOC procedure to address the fundamental computational bottleneck stemming from calculating the partition function via dynamic programming. Approximate MaxEnt IOC is ...

Maximum entropy (Maxent) is useful in natural language processing and many other areas. Iterative scaling (IS) methods are one of the most popular approaches to solve Maxent. With many variants of IS methods, it is difficult to understand them and see the differences. In this paper, we create a general and unified framework for iterative scaling methods. This framework also connects iterative s...

Understanding species response to climate change is essential for the conservation and utilization of resources under rapid in future. In this study, present future suitable distribution range Keteleeria davidiana, a tertiary relict gymnosperm, was predicted based on maximum entropy model (MaxEnt). A total 158 occurrence records were collected after removing duplicated records. Six low-correlat...

This work presents a comprehensive second-order predictive modeling (PM) methodology designated by the acronym 2nd-BERRU-PMD. The attribute “2nd” indicates that this incorporates uncertainties (means and covariances) sensitivities of computed model responses to parameters. BERRU stands for “Best- Estimate Results with Reduced Uncertainties” last letter (“D”) in “deterministic,” referring determ...

Species distribution modeling is an essential tool for understanding the ecology of species and has many applications in conservation. Using maximum entropy (MaxEnt) modeling, we identify key factors shaping potential endangered Javan Gibbons Hylobates moloch one main remnant habitats, Gunung Halimun Salak National Park (GHSNP), Indonesia, using presence-only data collected between October Nove...

A popular approach to apprenticeship learning (AL) is to formulate it as an inverse reinforcement learning (IRL) problem. The MaxEnt-IRL algorithm successfully integrates the maximum entropy principle into IRL and unlike its predecessors, it resolves the ambiguity arising from the fact that a possibly large number of policies could match the expert’s behavior. In this paper, we study an AL sett...