BACKGROUND A number of biotic and abiotic factors have been proposed as drivers of geographic variation in species richness. As biotic elements, inter-specific interactions are the most widely recognized. Among abiotic factors, in particular for plants, climate and topographic variables as well as their historical variation have been correlated with species richness and endemism. In this study,...

We consider a class of nonparametric time series regression models in which the regressor takes values in a sequence space and the data are stationary and weakly dependent. Technical challenges that hampered theoretical advances in these models include the lack of associated Lebesgue density and diffi culties with regard to the choice of dependence structure of the data generating process in th...

Using Matched Employer-Employee Data to Study Labor Market Discrimination Wage gaps between individuals of difference races, sexes, and ethnicities have been documented and replicated extensively, and have generated a long history in labor economics research of empirical tests for labor market discrimination. The most widely-used approach to test for labor market discrimination is based on wage...

We propose a modular neural-network structure for implementing the Bayesian framework for learning and inference. Our design has three main components, two for computing the priors and likelihoods based on observations and one for applying Bayes’ rule. Through comprehensive simulations we show that our proposed model succeeds in implementing Bayesian learning and inference. We also provide a no...

Default reasoning is a useful inference technique which involves choosing a single context in which further inferences are to be made. If this choice is incorrect, the context may need to be switched. Dependency-directed backtracking provides a method for such context switching. Doyle’s algorithm for dependency-directed backtracking is revised to allow context switching to be guided by the call...

In this report we summarize the recent paper [Taylor et al., 2014] which proposes new inference tools for methods that perform variable selection and estimation in an adaptive regression. Although this paper mainly studies forward stepwise regression (FS) and least angle regression (LAR), the approach in this paper is not limited to these cases. This paper describes how to carry out exact infer...

In this paper, we introduce and study a new paraconsistent inference relation |=c in the setting of 3-valued paraconsistent logics. Using inconsistency forgetting as a key mechanism for recovering consistency, it guarantees that the deductive closure Cn|=c(Σ) of any belief base Σ is classically consistent and classically closed. This strong feature, not shared by previous inference relations in...

Inference mechanisms and interpretations of fuzzy rule bases are studied together from the point of view of systems of fuzzy relation equations. A proper use of an inference mechanism connected to a fuzzy relation interpreting a fuzzy rule base is certified by keeping the fundamental interpolation condition. The paper aims at new solutions of systems of fuzzy relation equations which are motiva...

Bayesian inference has been shown to be an efficient mechanism for describing models of learning; however, concerns over a lack of constraint in Bayesian models (e.g., Jones & Love, 2011) has limited their influence as being a description of the ‘real’ processes of human cognition. In this paper, we review some of these concerns and argue that cognitive architectures can address these concerns ...

The question investigated in this paper is: How does ACT-R propitiate as an architecture for modeling the search and evaluation of (scientific) hypotheses? It is demonstrated how inductive search, modeled by Paul Thagard's PI and Herbert Simon's BACON.1, can easily be learned from examples by ACT-R 3.0 as a form of abductive inference. Furthermore, I argue that PI's hypothesis evaluation proces...