RG-inspired lattice models for piecewise GLMs provide explicit interpretable partitions and a replica-analysis-derived scaling law for regularization that allows increasing complexity without expected rise in generalization loss.
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An autoregressive Gaussian process transport-map construction factors spatio-temporal joint densities into conditional distributions with data-dependent sparsity to enable scalable generative modeling of non-Gaussian fields.
New expert-informed Bayesian priors and elicitation procedure for inferring single-subject functional connectivity graphs from resting-state fMRI, yielding posterior distributions over edge weights.
npde methods extend to joint longitudinal-TTE models via censored data imputation and a combined test that maintains ~5% type I error while detecting misspecifications in prostate cancer simulations.
The deep SPAR model shows concurrent floods and droughts becoming more likely in the Upper Danube by 2100 under high emissions, with changes in the dependence between catchments contributing substantially to the increase.
citing papers explorer
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A renormalization-group inspired lattice-based framework for piecewise generalized linear models
RG-inspired lattice models for piecewise GLMs provide explicit interpretable partitions and a replica-analysis-derived scaling law for regularization that allows increasing complexity without expected rise in generalization loss.
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Scalable generative modeling of non-Gaussian spatio-temporal fields via autoregressive Gaussian processes
An autoregressive Gaussian process transport-map construction factors spatio-temporal joint densities into conditional distributions with data-dependent sparsity to enable scalable generative modeling of non-Gaussian fields.
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Prior elicitation for Bayesian estimation of single-subject connectivity networks
New expert-informed Bayesian priors and elicitation procedure for inferring single-subject functional connectivity graphs from resting-state fMRI, yielding posterior distributions over edge weights.
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Evaluation of the npde performance for the evaluation of joint model with longitudinal and TTE data: an application in metastatic hormono-resistant prostate cancer
npde methods extend to joint longitudinal-TTE models via censored data imputation and a combined test that maintains ~5% type I error while detecting misspecifications in prostate cancer simulations.
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Exploring climate change effects on concurrent floods and concurrent droughts via statistical deep learning
The deep SPAR model shows concurrent floods and droughts becoming more likely in the Upper Danube by 2100 under high emissions, with changes in the dependence between catchments contributing substantially to the increase.