A projection posterior for exponentially tilted empirical likelihood that integrates generative AI auxiliary data, with new Bernstein-von Mises and consistency theorems under vanishing and persistent prior regimes.
RiskofBayesianinferenceinmisspecifiedmodels, andthesandwichcovariance matrix
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
NBPL uses a nonparametric Dirichlet process prior on the reduced-form distribution for posterior inference on optimal treatment assignments and welfare, with minimax-optimal regret convergence and pointwise consistent policy class comparisons.
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Empirical Likelihood with Generative AI
A projection posterior for exponentially tilted empirical likelihood that integrates generative AI auxiliary data, with new Bernstein-von Mises and consistency theorems under vanishing and persistent prior regimes.
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Nonparametric Bayesian Policy Learning
NBPL uses a nonparametric Dirichlet process prior on the reduced-form distribution for posterior inference on optimal treatment assignments and welfare, with minimax-optimal regret convergence and pointwise consistent policy class comparisons.