Derives heuristic coverage bounds for MLFriends nested sampling under a Binomial point process model, claiming the bias is negligible compared to statistical variance.
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A trans-dimensional Bayesian model set averaging framework for 13C-MFA that averages flux estimates over uncertain network topologies using reversible jump MCMC and diffusive nested sampling.
Slice Monte Carlo integration partitions parameter space with a surrogate to enable variance-reduced stratified Monte Carlo estimation of integrals involving expensive target functions.
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First analytical coverage bounds of a fully specified nested sampling algorithm
Derives heuristic coverage bounds for MLFriends nested sampling under a Binomial point process model, claiming the bias is negligible compared to statistical variance.