Relevance and truthfulness in information correction and fusion

A general approach to information correction and fusion for belief functions is proposed, where not only may the information items be irrelevant, but sources may lie as well. We introduce a new correction scheme, which takes into account uncertain metaknowledge on the source’s relevance and truthfulness and that generalizes Shafer’s discounting operation. We then show how to reinterpret all connectives of Boolean logic in terms of source behavior assumptions with respect to relevance and truthfulness. We are led to generalize the unnormalized Dempster’s rule to all Boolean connectives, while taking into account the uncertainties pertaining to assumptions concerning the behavior of sources. Eventually, we further extend this approach to an even more general setting, where source behavior assumptions do not have to be restricted to relevance and truthfulness. We also establish the commutativity property between correction and fusion processes, when the behaviors of the sources are independent.     

Rejoinder on “Statistical reasoning with set-valued information: Ontic vs. epistemic views”

This note replies to comments made on our contribution to the Low Quality Data debate.     

A choice model with imprecise ordinal evaluations

We consider multicriteria choice problems where the actions are evaluated on ordinal criteria and where they can be assessed imprecisely. In order to select the subset of best actions, the pairwise comparisons between the actions on each criterion are modeled by basic belief assignments (BBAs). Dempsterʼs rule of combination is used for the aggregation of the BBAs of each pair of alternatives in order to express a global comparison between them on all the criteria. A model inspired by ELECTRE I is also proposed and illustrated by a pedagogical example.     

Insuring against loss of evidence in game-theoretic probability

Statistical testing can be framed as a repetitive game between two players, Forecaster and Sceptic. In each round, Forecaster sets prices for various gambles, and Sceptic chooses which gambles to make. If Sceptic multiplies by a large factor the capital he puts at risk, he has evidence against Forecaster’s ability. His capital at the end of each round is a measure of his evidence against Forecaster so far. This can go up and then back down. If you report the maximum so far instead of the current value, you are exaggerating the evidence against Forecaster. In this article, we show how to remove the exaggeration. Removing it means systematically reducing the maximum in such a way that a rival to Sceptic can always play so as to obtain current evidence as good as Sceptic’s reduced maximum. We characterize the functions that can achieve such reductions. Because these functions may impose only modest reductions, we think of our result as a method of insuring against loss of evidence. In the context of an actual market, it is a method of insuring against the loss of what an investor has gained so far.     

专家系统中证据的合成,传播与修正

在专家系统中，由于证据的不确定性和推理规则的不确定性，推理也相应地发生变化，需要对证据进行合成、传播与修正。有许多文献进行了研究［２］，［３］，［４］，但已有的方法大都是针对不同的不确定性推理给出不同的方法。“本文旨在给出不确定性推理中证据合成、传播与修正的一般公式。     

Prediction of future observations using belief functions: A likelihood-based approach

We study a new approach to statistical prediction in the Dempster–Shafer framework. Given a parametric model, the random variable to be predicted is expressed as a function of the parameter and a pivotal random variable. A consonant belief function in the parameter space is constructed from the likelihood function, and combined with the pivotal distribution to yield a predictive belief function that quantifies the uncertainty about the future data. The method boils down to Bayesian prediction when a probabilistic prior is available. The asymptotic consistency of the method is established in the iid case, under some assumptions. The predictive belief function can be approximated to any desired accuracy using Monte Carlo simulation and nonlinear optimization. As an illustration, the method is applied to multiple linear regression.     

火场残留无痕迹燃烧物的物证分析(Ⅰ)

基于硫酸介质中重铬酸钾氧化乙醇的反应,建立了火场残留无痕迹助燃剂乙醇的定性和定量分析方法。定性分析检出限量5μg,最低检出浓度0.20mg.L-1。定量分析的检出限1.22mg.L-1,线性范围为0.10~3.2000g.L-1。将方法用于火灾现场残留的乙醇分析,结果满意。     

火场残留无痕迹燃烧物的物证分析(Ⅱ)

基于盐酸羟胺对丙酮的吸收使体系pH减小,体系中丙酮浓度与吸光度之间呈线性关系,建立火场残留无痕迹助燃剂丙酮的定性和定量分析方法。定性分析的检出限为6.15μg,最低浓度为0.53 mg.L-1。定量分析的检出限为1.53 mg.L-1,线性范围4.0~60.0 mg.L-1。将方法用于火灾现场残留物中丙酮的分析,结果满意。     

基于D-S证据理论的Bayes信息融合

在Bayes统计中,验前分布的获取和表示是一个关键问题,尤其在验前信息多源型的情况下,更需要合理客观地综合利用这些信息.针对实际现实中信息的多源性和不确定性,本文给出了一种基于D-S证据理论的多源总体验前分布的融合方法,并通过实例说明了该方法在Bayes先验信息融合中的应用.