We defend a set of acceptance rules that avoids the lottery paradox, that is closed under classical entailment, and that accepts uncertain propositions without ad hoc restrictions. We show that the rules we recommend provide a semantics that validates exactly Adams’ conditional logic and are exactly the rules that preserve a natural, logical structure over probabilistic credal states that we ca...

avicenna has divided the conditionals to the real cogent (i.e. with possible antecedent) and the verbal cogent (i.e. with the impossible antecedent). denying the truth of the latter, he caused controversies among moslem logicians. at the paper, we report the controversies, analyze the real and verbal cogent conditionals, formalize them in modern modal and relevant logics, and finally, defend id...

Multivariate models are of great importance in theoretical and applied quantitative genetics. We extend quantitative genetic theory to accommodate situations in which there is linear feedback or recursiveness between the phenotypes involved in a multivariate system, assuming an infinitesimal, additive, model of inheritance. It is shown that structural parameters defining a simultaneous or recur...

We present a logic for belief revision in which revision of a theory by a sentence is represented using a conditional connective. The conditional is not primitive, but rather defined using two unary modal operators. Our approach captures and extends the classic AGM model without relying on the Limit Assumption. Reasoning about counterfactual or hypothetical situations is also crucial for AI. Ex...

An enormous amount of publications deals with smoothing in the sense of nonparametric regression. However, nearly all of the literature treats the case where predictors and response are related in the form of a function y = m(x) + noise. In many situations this simple functional model does not capture adequately the essential relation between predictor and response. We show by means of speed-fl...

We consider the problem of learning causal information between random variables in directed acyclic graphs (DAGs) when allowing arbitrarily many latent and selection variables. The FCI (Fast Causal Inference) algorithm has been explicitly designed to infer conditional independence and causal information in such settings. However, FCI is computationally infeasible for large graphs. We therefore ...

We propose a new methodology to asses risk spillovers in time-series framework. Firstly, we introduce an explicit nonparametric measure of cross-sectional conditional tail co-movement, which is intuitively comparable the Conditional Value-at-Risk (CoVaR). show that nonlinear CoVaR (NCoVaR) able capture even highly dependence structures. Secondly, for purpose potential contagion analysis, adapt ...

We consider situations in which a significant proportion of observations in a time series are zero, but the remaining observations are positive and measured on a continuous scale. We propose a new dynamic model in which the conditional distribution of the observations is constructed by shifting a distribution for non-zero observations to the left and censoring negative values. The key to genera...

Construction equipment is an important type of resources of heavy construction contractors. Since equipment breakdowns can cause project delays and significant financial losses, the contractors are eager to know those factors causing equipment failures directly or indirectly, related to equipment design, maintenance, and operations. Although Bayesian network can be used for diagnostic analysis ...