8 papers
Fast boundary-aware spatial intensity estimation on complex domains
Takumi Nakagawa, KÅsaku Takanashi, Kenichiro McAlinn +1
Spatial intensity maps are routinely used to summarize point patterns on geographically constrained regions, such as islands, coastlines, watersheds, ecological reserves, and admin…
An Entropy-Energy Identity for Predictive Kullback-Leibler Regret in Infinitely Divisible Location Models
KÅsaku Takanashi, Kenichiro McAlinn
We consider predictive density estimation under logarithmic score for -dimensional infinitely divisible location models. Taking the formal Bayes predictive density under the Leb…
When Is Generalized Bayes Bayesian? A Decision-Theoretic Characterization of Loss-Based Updating
Kenichiro McAlinn, KÅsaku Takanashi
Loss-based updating, including generalized Bayes, Gibbs, and quasi-posteriors, replaces likelihoods by a user-chosen loss and produces a posterior-like distribution via exponential…
Dynamic causal inference with time series data
Tanique Schaffe-Odeleye, KÅsaku Takanashi, Vishesh Karwa +2
We generalize the potential outcome framework to time series with an intervention by defining causal effects on stochastic processes. Interventions in dynamic systems alter not onl…
Optimal Hold-Out Size in Cross-Validation
Kenichiro McAlinn, KÅsaku Takanashi
Cross-validation (CV) is routinely used across the sciences to select models and tune parameters, and the resulting choices are often interpreted as substantive scientific conclusi…
Bayesian Spatial Predictive Synthesis
Danielle Cabel, Shonosuke Sugasawa, Masahiro Kato +2
Due to spatial dependence -- often characterized as complex and non-linear -- model misspecification is a prevalent and critical issue in spatial data analysis and prediction. As t…