SurvivalPFN amortizes Bayesian survival analysis for right-censored data by pretraining a prior-data fitted network on synthetic identifiable DGPs and then performing in-context inference, achieving competitive results on 61 real datasets.
Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.Statistics in medicine, 15(4):361–387, 1996
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SDPM is a diffusion probabilistic model that generates continuous survival times and censoring indicators to estimate survival functions without parametric assumptions or time discretization.
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SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference
SurvivalPFN amortizes Bayesian survival analysis for right-censored data by pretraining a prior-data fitted network on synthetic identifiable DGPs and then performing in-context inference, achieving competitive results on 61 real datasets.
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SDPM: Survival Diffusion Probabilistic Model for Continuous-Time Survival Analysis
SDPM is a diffusion probabilistic model that generates continuous survival times and censoring indicators to estimate survival functions without parametric assumptions or time discretization.