This paper discusses an approach for treating model uncertainties in relation to quantitative risk assessments. The analysis is based on a conceptual framework where a distinction is made between model error–the difference between the model prediction and the true future quantity–and model output uncertainty–the (epistemic) uncertainty about the magnitude of this error. The aim of the paper is ...

The primary theme of this investigation is a decision theoretic account of conditional ought statements (e.g., "You ought to do A, if C") that rectifies glaring deficiencies in classical deontic logic. The resulting account forms a sound basis for qualitative decision theory, thus providing a framework for qualitative planning under uncertainty. In particular, we show that adding causal relatio...

Abstract We introduce a framework for statistical inference of the closure coefficients using machine learning methods. The objective this is to quantify epistemic uncertainty associated with model by experimental data via Bayesian statistics. tailored towards cases which limited amount available. It consists two components. First, treating all latent variables (non-observed variables) in as st...

Building performance is often expressed according to primary energy use; however studies should also include environmental load. To mitigate the effects of increased CO2 emissions, green building designs are now increasingly popular options for stakeholders. In contrast, impacts design parameters, assessment, and optimization objectives not addressed sufficiently American infrastructure. Design...

This paper introduces the concept of conditional independence in valuation-based systems (VBS). VBS is an axiomatic framework capable of representing many different uncertainty calculi. We define conditional independence in terms of factorization of the joint valuation. The definition of conditional independence in VBS generalizes the corresponding definition in probability theory. Besides prob...