947 research outputs found
Kuznetsov independence for interval-valued expectations and sets of probability distributions: Properties and algorithms
Kuznetsov independence of variables X and Y means that, for any pair of bounded functions f(X)f(X) and g(Y)g(Y), E[f(X)g(Y)]=E[f(X)]⊠E[g(Y)]E[f(X)g(Y)]=E[f(X)]⊠E[g(Y)], where E[⋅]E[⋅] denotes interval-valued expectation and ⊠ denotes interval multiplication. We present properties of Kuznetsov independence for several variables, and connect it with other concepts of independence in the literature; in particular we show that strong extensions are always included in sets of probability distributions whose lower and upper expectations satisfy Kuznetsov independence. We introduce an algorithm that computes lower expectations subject to judgments of Kuznetsov independence by mixing column generation techniques with nonlinear programming. Finally, we define a concept of conditional Kuznetsov independence, and study its graphoid properties.ThefirstauthorhasbeenpartiallysupportedbyCNPq,andthisworkhasbeensupportedbyFAPESPthroughgrant04/09568-0.ThesecondauthorhasbeenpartiallysupportedbytheHaslerFoundationgrantno.10030
Optimal Uncertainty Quantification
We propose a rigorous framework for Uncertainty Quantification (UQ) in which
the UQ objectives and the assumptions/information set are brought to the forefront.
This framework, which we call Optimal Uncertainty Quantification (OUQ), is based
on the observation that, given a set of assumptions and information about the problem,
there exist optimal bounds on uncertainties: these are obtained as extreme
values of well-defined optimization problems corresponding to extremizing probabilities
of failure, or of deviations, subject to the constraints imposed by the scenarios
compatible with the assumptions and information. In particular, this framework
does not implicitly impose inappropriate assumptions, nor does it repudiate relevant
information.
Although OUQ optimization problems are extremely large, we show that under
general conditions, they have finite-dimensional reductions. As an application,
we develop Optimal Concentration Inequalities (OCI) of Hoeffding and McDiarmid
type. Surprisingly, contrary to the classical sensitivity analysis paradigm, these results
show that uncertainties in input parameters do not necessarily propagate to
output uncertainties.
In addition, a general algorithmic framework is developed for OUQ and is tested
on the Caltech surrogate model for hypervelocity impact, suggesting the feasibility
of the framework for important complex systems
Jensen's and Cantelli's Inequalities with Imprecise Previsions
We investigate how basic probability inequalities can be extended to an
imprecise framework, where (precise) probabilities and expectations are
replaced by imprecise probabilities and lower/upper previsions. We focus on
inequalities giving information on a single bounded random variable ,
considering either convex/concave functions of (Jensen's inequalities) or
one-sided bounds such as or (Markov's and Cantelli's
inequalities). As for the consistency of the relevant imprecise uncertainty
measures, our analysis considers coherence as well as weaker requirements,
notably -coherence, which proves to be often sufficient. Jensen-like
inequalities are introduced, as well as a generalisation of a recent
improvement to Jensen's inequality. Some of their applications are proposed:
extensions of Lyapunov's inequality and inferential problems. After discussing
upper and lower Markov's inequalities, Cantelli-like inequalities are proven
with different degrees of consistency for the related lower/upper previsions.
In the case of coherent imprecise previsions, the corresponding Cantelli's
inequalities make use of Walley's lower and upper variances, generally ensuring
better bounds.Comment: Published in Fuzzy Sets and Systems -
https://dx.doi.org/10.1016/j.fss.2022.06.02
Modernization of the Russian social policy : Social crisis, interventions, and withdrawals
Peer reviewe
PSA 2018
These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2018
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