This paper presents new inference algorithms based on rules partition. Optimisation relies on reducing the number of rules searched in each run of inference and reducing the number of unnecessary recursive calls and avoiding the extraction of too many new facts. The first part of the paper presents the definition of the model of knowledge base based on rules partition. The second part describes...

We present a “conditional game” to be played between two approximate inference algorithms. We prove that exact inference is an optimal strategy and demonstrate how the game can be used to estimate the relative accuracy of two different approximations in the absence of exact marginals.

This paper synthesizes a recent line of work on automated predictive model making inspired by Rate-Distortion theory, in particular by the Information Bottleneck method. Predictive inference is interpreted as a strategy for efficient communication. The relationship to thermodynamic efficiency is discussed. The overall aim of this paper is to explain how this information theoretic approach provi...

Intuitive multistage inferences are typically excessive when compared with the optimal model, a modified form of Bayes' theorem. One explanation for this excessiveness is that the S primarily attends to the implications of the probable event described by the first-stage inference, neglecting the implications of less likely events. If a S follows this strategy, called a "best guess" strategy, th...

We present a new statistical framework for stochastic grammatical inference algorithms based on a state merging strategy. We propose to use multinomial statistical tests to decide which states should be merged. This approach has three main advantages. First, since it is not based on asymptotic results, small sample case can be specifically dealt with. Second, all the probabilities associated to...

Fairness is an important concept emerged in theorem proving recently, in particular in the area of completion-based theorem proving. Fairness is a required property for the search plan of the given strategy. Intuitively, fairness of a search plan guarantees the generation of a successful derivation if the inference mechanism of the strategy indicates that there is one. Thus, the completeness of...

We propose a system named AIGNET (Algorithms for Inference of Genetic Networks), and introduce two top-down approaches for the inference of interrelated mechanism among genes in genetic network that is based on the steady state and temporal analyses of gene expression patterns against some kinds of gene perturbations such as disruption or overexpression. The former analysis is performed by a st...

Recent algorithms for model counting and compilation work by decomposing a CNF into syntactically independent components through variable splitting, and then solving the components recursively and independently. In this paper, we observe that syntactic component analysis can miss decomposition opportunities because the syntax may hide existing semantic independence, leading to unnecessary varia...

ABSTRACT: This article presents the development of a general Bayes inference model for accelerated life testing. The failure times at a constant stress level are assumed to belong to a Weibull distribution, but the specification of strict adherence to a parametric time-transformation function is not required. Rather, prior information is used to indirectly define a multivariate prior distributi...