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Likelihood-Coin Poisson Sampling for Bayesian Inverse Problems: Exact Sampling and Sharp Complexity
We develop an exact direct-sampling framework for Bayesian inverse problems whose bounded forward observables can be queried through Bernoulli events rather than numerically evaluated. A Bernstein--Poisson construction converts these events into scaled Gaussian likelihood coins. Thinning a prior-bas
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