Stabilised weighted subsampling yields an unbiased log-likelihood estimator for recursive models that reduces recursion depth and computational cost while avoiding variance inflation via principled decay restrictions.
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A new modular approximate Bayesian inference approach generates probabilistic option price predictions from multiple data sources using only the theoretical pricing model, without an explicit statistical model for option prices.
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Stabilised weighted data subsampling for accelerated inference in models with recursive likelihoods
Stabilised weighted subsampling yields an unbiased log-likelihood estimator for recursive models that reduces recursion depth and computational cost while avoiding variance inflation via principled decay restrictions.
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Probabilistic Predictions of Option Prices with Modular Approximate Bayesian Inference
A new modular approximate Bayesian inference approach generates probabilistic option price predictions from multiple data sources using only the theoretical pricing model, without an explicit statistical model for option prices.