A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
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Develops clr-based local indicators of mark association for composition-valued marks in spatial point processes to detect local heterogeneity invisible to global metrics.
A Bayesian model for multi-feature contact matrices that uses tensor structures and contingency table theory to satisfy structural constraints and impute missing contact features, validated on simulations and US/German survey data.
Graph nodes are embedded as simplex compositions via ILR coordinates in Aitchison geometry to obtain interpretable representations that support component restriction and competitive task performance.
citing papers explorer
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Bayesian Global Fr\'echet Regression via Weak Conditional Expectations
A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
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Uncovering Local Heterogeneity: Local Summary Characteristics for Spatial Point Processes with Composition-Valued Marks
Develops clr-based local indicators of mark association for composition-valued marks in spatial point processes to detect local heterogeneity invisible to global metrics.
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Bayesian Modeling and Prediction of Generalized Contact Matrices
A Bayesian model for multi-feature contact matrices that uses tensor structures and contingency table theory to satisfy structural constraints and impute missing contact features, validated on simulations and US/German survey data.
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Aitchison Embeddings for Learning Compositional Graph Representations
Graph nodes are embedded as simplex compositions via ILR coordinates in Aitchison geometry to obtain interpretable representations that support component restriction and competitive task performance.
- Forecasting the Evolving Composition of Inbound Tourism Demand: A Bayesian Compositional Time Series Approach Using Platform Booking Data