A unified Laplace approximation error bound with a tunable matrix D recovers prior bounds and yields an order-of-magnitude tighter, dimension-free estimate in a Bayesian inverse problem.
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A unified theory of the high-dimensional Laplace approximation with application to Bayesian inverse problems
A unified Laplace approximation error bound with a tunable matrix D recovers prior bounds and yields an order-of-magnitude tighter, dimension-free estimate in a Bayesian inverse problem.