Karl Havens (1) infers that 50 highly and evenly resolved food webs confirm the convention (2) that seven food web properties are "scale-invariant" or roughly constant among food webs with widely varying numbers of species. Also in line with the convention, an eighth food web property, connectance, is inferred to be scale-dependent and to decrease as the number of species increases (2). I have ...

Estimating the parameters of a regression model of interest is complicated by missing data on the variables in that model. Multiple imputation is commonly used to handle these missing data. Joint model multiple imputation and full-conditional specification multiple imputation are known to yield imputed data with the same asymptotic distribution when the conditional models of full-conditional sp...

This article (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the invariance of error distributions may be essential for causal inference, but the errors themselves need not be invariant. Graphs can be interpreted using conditional distributions, so that we can better address connections be...

in this paper, we consider the production possibility set with n production units such that the following four principles that governs: inclusion observations, conceivability, immensity and convexity. our goal is to estimate the output of a same and new production unit with existing production possibility and amount of input is specified. so, initially we find the interval changes of each input...

Bayesian inference is an important technique throughout statistics. The essence of Beyesian inference is to derive the posterior belief updated from prior belief by the learned information, which is a set of differentially private answers under differential privacy. Although Bayesian inference can be used in a variety of applications, it becomes theoretically hard to solve when the number of di...

While hypothetical reasoning is a useful knowledge system framework that is applicable to many practical problems, its crucial problem is its slow inference speed. The paper presents a speedup method for hypothetical reasoning systems that uses an experience-based learning mechanism which can learn knowledge from inference experiences to improve the inference speed in subsequent inference proce...