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Status: Unchecked ×Keyword: sequential Monte Carlo ×Clear all

2 claims from 1 paper

1. Mathematics › Markov Chains and Monte Carlo Methods

   Prequential posteriors

   Roy, Everitt, Robert and Dutta · Japanese Journal of Statistics and Data Science · 2026

   The paper introduces prequential posteriors for assimilating data into forecasting models with intractable likelihoods, proves a consistency result, and tests the method on synthetic and meteorological data.

   Unchecked2 claims
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   1. UncheckedUnder mild conditions, prequential loss minimisers and posteriors concentrate near parameters with the best predictive performance, even if the model is misspecified.“Since the true data-generating process often lies outside the assumed model class, we adopt an alternative notion of consistency and prove that, under mild conditions, both the prequential loss minimizer and the prequential posterior concentrate around parameters with optimal predictive performance.”
   2. Unchecked“For scalable inference, we employ easily parallelizable wastefree sequential Monte Carlo (SMC) samplers with preconditioned gradient-based kernels, enabling efficient exploration of high-dimensional parameter spaces such as those in DGFMs.”