Species distribution models have great potential to efficiently guide management for threatened species, especially for those that are rare or cryptic. We used MaxEnt to develop a regional-scale model for the koala Phascolarctos cinereus at a resolution (250 m) that could be used to guide management. To ensure the model was fit for purpose, we placed emphasis on validating the model using indep...

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...

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...

Sapindus delavayi (Franch.) Radlk. (S. delavayi) is an important biological washing material and biomass energy tree species with a peel rich in saponins kernel high oil content. We used the maximum entropy model (MaxEnt) to predict suitable habitats for S. China, screen dominant environmental factors affecting its distribution, analyze changes under future climate change. The results provide s...

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 ...

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...

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...

Maximum entropy (Maxent) modeling was used to predict the potential climatic niches of three medicinally important Asclepiad species: Pentatropis spiralis, Tylophora hirsuta, and Vincetoxicum arnottianum. All three species are members of the Asclepiad plant family, yet they differ in ecological requirements, biogeographic importance, and conservation value. Occurrence data were collected from h...

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...