We introduce the Hamming ball sampler, a novel Markov chain Monte Carlo algorithm, for efficient inference in statistical models involving high-dimensional discrete state spaces. The sampling scheme uses an auxiliary variable construction that adaptively truncates the model space allowing iterative exploration of the full model space. The approach generalizes conventional Gibbs sampling schemes...

Standard approaches for inference in probabilistic formalisms with first-order constructs include lifted variable elimination (LVE) for single queries. To handle multiple queries efficiently, the lifted junction tree algorithm (LJT) uses a first-order cluster representation of a knowledge base and LVE in its computations. We extend LJT with a full formal specification of its algorithm steps inc...

Typing checking is an efficient way to catch simple errors. Refinement types [3] are invented to take advantage of type systems in specifying more program properties and verifying them through type checking. In [3, 2], a method to use rectype declaration has been proposed to specify regular tree grammar based invariants. Datatype lattices are then derived from rectype declarations. An algorithm...

In this paper, an adaptive channel estimation scheme based on the reduced-rank (RR) Wiener filtering (WF) technique is proposed for multi-band (MB) orthogonal frequency division multiplexing (OFDM) ultra-wideband (UWB) communication systems in multipath fading channels. This RR-WF-based algorithm employs an adaptive fuzzy-inference-controlled (FIC) filter rank. Additionally, a comparative inves...

We propose an algorithm for exploiting statistical properties of large-scale metadata databases about music titles to answer musicological queries. We introduce two inference schemes called “direct” and “inverse” inference, based on an efficient implementation of a kernel regression approach. We describe an evaluation experiment conducted on a large-scale database of finegrained musical metadat...

Pawlak recently introduced rough set flow graphs (RSFGs) as a graphical framework for reasoning from data. No study, however, has yet investigated the complexity of the accompanying inference algorithm, nor the complexity of inference in RSFGs. In this paper, we show that the traditional RSFG inference algorithm has exponential time complexity. We then propose a new RSFG inference algorithm tha...

In this paper, we present a new path-following interior-point algorithm for -horizontal linear complementarity problems (HLCPs). The algorithm uses only full-Newton steps which has the advantage that no line searchs are needed. Moreover, we obtain the currently best known iteration bound for the algorithm with small-update method, namely, , which is as good as the linear analogue.

This research aims to estimate the overflow capacity of a curved labyrinth using different intelligent prediction models, namely adaptive neural-fuzzy inference system, support vector machine, M5 model tree, least-squares machine and machine–bat algorithm (LSSVM-BA). A total 355 empirical data for 6 congressional models were extracted from results laboratory study on models. The parameters upst...

Inductive Logic Programming (ILP) deals with inducing clausal theories from examples basically through generalization or specialization. The specialization and generalization operators used are mainly based on three generality orderings subsumption, implication and implication relative to background knowledge. Implication is stronger than subsumption, but relative implication is more powerful b...