This article offers a new paradigm for analyzing the behavior of uncertain
multivariable systems using a set of quantities we call \emph{inferential
moments}. Marginalization is an uncertainty quantification process that
averages conditional probabilities to quantify the \emph{expected value} of a
probability of interest. Inferential moments are higher order conditional
probability moments that describe how a distribution is expected to respond to
new information. Of particular interest in this article is the
\emph{inferential deviation}, which is the expected fluctuation of the
probability of one variable in response to an inferential update of another. We
find a power series expansion of the Mutual Information in terms of inferential
moments, which implies that inferential moment logic may be useful for tasks
typically performed with information theoretic tools. We explore this in two
applications that analyze the inferential deviations of a Bayesian Network to
improve situational awareness and decision-making. We implement a simple greedy
algorithm for optimal sensor tasking using inferential deviations that
generally outperforms a similar greedy Mutual Information algorithm in terms of
predictive probabilistic error.

该文章提出了使用推断矩的一种新范例分析不确定的多变量系统的行为。推断矩可以更好地描述分布对新信息的响应，同时可以应用于改进感知和决策。