background: in the case of sensitive questions such as number of alcoholics known, majority of respondents might give an answer of zero. poisson regression model (p) is the standard tool to analyze count data. however, p provides poor fit in the case of zero inflated counts, when over-dispersion exists. therefore, the questions to be addressed are to compare performance of alternative count reg...

Distance sampling is a wildlife sampling technique used to estimate population size or density. Describing how density varies spatially is often of equal interest; however, conventional methods of analysis do not allow for explicit modeling of both density and detection probability. The function distsamp implements the multinomial-Poisson mixture model of Royle et al. (2004), which was develope...

In this poster we study active learning for supervised regression algorithms. We focus on a particular problem where the regression is constrained to an interval. This is the case, for instance, when the objective is to estimate the probability of success of a certain event given a set of input points. The regression problem is, thus, constrained to the zero-one interval and the training exampl...

this study was carried out to evaluate the ekiti state social security scheme (essss) in nigeria. specifically, the study estimated the food security status of the beneficiaries and non beneficiaries of the scheme, assessed the effect of the social security scheme on households’ food security status, and identified the constraints to the implementation of the scheme in the state. a three stage ...

wild goat (capra aegagrus) is one of the most prominent mountain mammals dispersed in many habitats in iran. however, few studies on the relationship between this species and its habitat have been conducted. this species is recognized as one of the main food source for highly endangered species such as cheetah and leopard. in this study which lasted from fall 2009 through summer 2011, seasonal ...

Probabilistic forecasts of atmospheric variables are often given as relative frequencies obtained from ensembles of deterministic forecasts. The detrimental effects of imperfect models and initial conditions on the quality of such forecasts can be mitigated by calibration. This paper shows that Bayesian methods currently used to incorporate prior information can be written as special cases of a...