For high-dimensional data sets with complicated dependency structures, the full likelihood approach often leads to intractable computational complexity. This imposes difficulty on model selection, given that most traditionally used information criteria require evaluation of the full likelihood. We propose a composite likelihood version of the Bayes information criterion (BIC) and establish its ...

It is important to identify residual slow conduction and minimize the chance of resumption of conduction after right atrial isthmus ablation to reduce the chance of recurrence of atrial flutter (AFL). The aim of this article is to discuss the best possible way of confirming a bi-directional isthmus conduction (BIC) block after ablation of an isthmus-dependent AFL. A combination of activation an...

A bound state in the continuum (BIC) is a localized continuous spectrum of radiating waves. In this study, coupled-mode theory introduced to more precisely interpret symmetry-protected BIC and quasi-BIC facile trimeric metasurface, compared with Fano formula, which generate high-quality factor resonance metallic slit metasurface. The multipole decomposition method near field distribution struct...

This paper presents an effective method for clustering unknown speech utterances based on their associated speakers. The proposed method jointly optimizes the generated clusters and the required number of clusters according to a Bayesian information criterion (BIC). The criterion assesses a partitioning of utterances based on how high the level of within-cluster homogeneity can be achieved at t...

The data driven Neyman statistic consists of two elements: a score statistic in a finite dimensional submodel and a selection rule to determine the best fitted submodel. For instance, Schwarz BIC and Akaike AIC rules are often applied in such constructions. For moderate sample sizes AIC is sensitive in detecting complex models, while BIC works well for relatively simple structures. When the sam...

Probability trees (or Probability Estimation Trees, PET’s) are decision trees with probability distributions in the leaves. Several alternative approaches for learning probability trees have been proposed but no thorough comparison of these approaches exists. In this paper we experimentally compare the main approaches using the relational decision tree learner Tilde (both on non-relational and ...

In this paper, we propose an eÆcient approach for unsupervised audio stream segmentation and clustering via the Bayesian Information Criterion (BIC). The proposed method extends an earlier formulation by Chen and Gopalakrishnan[1]. In our segmentation formulation, Hotelling's T -Statistic is used to pre-select candidate segmentation boundaries followed by BIC to make the segmentation decision. ...

Model selection is commonly based on some variation of the BIC or minimum message length criteria, such as MML and MDL. In either case the criterion is split into two terms: one for the model (data code length/model complexity) and one for the data given the model (message length/data likelihood). For problems such as change detection, unsupervised segmentation or data clustering it is common p...

A micro-calorimeter, suitable for detecting rates of heat production of the order of 1 mcal/h in about 2*3 ml. fluid, is described. Measurements have been made with it of the anaerobic heat production of bull spermatozoa in semen. The result, for the first hour, 220+ 15 mcal/109 live spermatozoa/h at 37° C, is consistent with the hypothesis that, in the first hour of incubation, the heat produc...

The concept of optical bound states in the continuum (BICs) underpins existence strongly localized waves embedded into radiation spectrum that can enhance electromagnetic fields subwavelength photonic structures. Early studies BICs waveguides and crystals uncovered their topological properties, quasi-BIC metasurfaces facilitated applications strong light-matter interactions to biosensing, lasin...