Danna L. Cruz-Reyes | Searcharxiv

arXiv2026

Directional Asymmetry in Edge BasedSpatial Models via a Skew Normal Prior

We introduce a skewed edge based spatial prior, named RENeGe sk that extends the Gaussian RENeGe framework by incorporating directional asymmetry through a skew normal distribution. Skewness is defined on the edge graph and propagated to the node space, aligning asymmetric behavior with transitions across neighboring regions rather than with marginal node effects. The model is formulated within the skew normal framework and employs identifiable hierarchical priors together with low rank parameterizations to ensure scalability. The skew normal's stochastic representation is considered to facilitate the computational implementation. Simulation studies show that RENeGe sk recovers compact, edge-aligned directional structure more accurately than symmetric Gaussian priors, while remaining competitive under irregular spatial patterns. An application to cancer incidence data in Southern Brazil illustrates how the proposed approach yields stable area-level estimates while preserving localized, directionally driven spatial variation.

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arXiv2025

Peace Sells, But Whose Songs Connect? Bayesian Multilayer Network Analysis of the Big 4 of Thrash Metal

We propose a Bayesian framework for multilayer song similarity networks and apply it to the complete studio discographies of the "Big 4" of thrash metal (Metallica, Slayer, Megadeth, Anthrax). Starting from raw audio, we construct four feature-specific layers (loudness, brightness, tonality, rhythm), augment them with song exogenous information, and represent each layer as a k-nearest neighbor graph. We then fit a family of hierarchical probit models with global and layer-specific baselines, node- and layer-specific sociability effects, dyadic covariates, and alternative forms of latent structure (bilinear, distance-based, and stochastic block communities), comparing increasingly flexible specifications using posterior predictive checks, discrimination and calibration metrics (AUC, Brier score, log-loss), and information criteria (DIC, WAIC). Across all bands, the richest stochastic block specification attains the best predictive performance and posterior predictive fit, while revealing sparse but structured connectivity, interpretable covariate effects (notably album membership and temporal proximity), and latent communities and hubs that cut across albums and eras. Taken together, these results illustrate how Bayesian multilayer network models can help organize high-dimensional audio and text features into coherent, musically meaningful patterns.

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