In this paper, a class of conjugate prior for estimating incomplete count data based on a broad class of conjugate prior distributions is presented. The new class of prior distributions arises from a conditional perspective, making use of the conditional specification methodology and can be considered as the generalisation of the form of prior distributions that have been used previously in the...

This paper addresses the problem of collective decision making in the case where the agents’ preferences are represented by CP-nets (Conditional Preference Networks). Most existing works either do not consider the computational issues, or depend on a strong assumption that all the agents share a common preferential independence structure. To this end, this paper proposes an efficient approach, ...

In this paper we study the relationship between the notion of coherence for conditional prevision assessments on a family of finite conditional random quantities and the notion of admissibility with respect to bounded strictly proper scoring rules. Our work extends recent results given by the last two authors of this paper on the equivalence between coherence and admissibility for conditional p...

We show that there can be no finite list of conditional independence relations which can be used to deduce all conditional independence implications among Gaussian random variables. To do this, we construct, for each n > 3 a family of n conditional independence statements on n random variables which together imply that X1⊥X2, and such that no subset have this same implication. The proof relies ...

The minimum error entropy (MEE) estimation is concerned with the estimation of a certain random variable (unknown variable) based on another random variable (observation), so that the entropy of the estimation error is minimized. This estimation method may outperform the well-known minimum mean square error (MMSE) estimation especially for non-Gaussian situations. There is an important performa...

The paper proposes an information-theory-based method for feature types analysis in probabilistic evaluation modelling for statistical parsing. The basic idea is that we use entropy and conditional entropy to measure whether a feature type grasps some of the information for syntactic structure prediction. Our experiment quantitatively analyzes several feature types’ power for syntactic structur...

The existing upper and lower bounds between entropy and error are mostly derived through an inequality means without linking to joint distributions. In fact, from either theoretical or application viewpoint, there exists a need to achieve a complete set of interpretations to the bounds in relation to joint distributions. For this reason, in this work we propose a new approach of deriving the bo...

In this paper, the redundancy of both variable and fixed rate Slepian–Wolf coding is considered. Given any jointly memoryless source-side information pair with finite alphabet, the redundancy of variable rate Slepian–Wolf coding of  with decoder only side information depends on both the block length and the decoding block error probability , and is defined as the difference between the minimum...

If surfaces from a scene are visually indistinguishable under one light, they may become distinguishable under another. The aim here was to test whether the frequency of such metamerism can be predicted by a statistical property of the colours of a scene, namely their conditional entropy. Simulations were based on 50 hyperspectral images of natural scenes under sunlight and north skylight. The ...