gemlib.mcmc supplies composable kernel abstractions for Metropolis-within-Gibbs sampling via writer monads, allowing concise expression and reuse of complex MCMC algorithms for partially observed epidemic models.
The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
2 Pith papers cite this work. Polarity classification is still indexing.
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LDDMM distances enable interpretable Bayesian calibration and posterior prediction for infinite-dimensional computer model outputs.
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gemlib.mcmc: composable kernels for Metropolis-within-Gibbs sampling schemes
gemlib.mcmc supplies composable kernel abstractions for Metropolis-within-Gibbs sampling via writer monads, allowing concise expression and reuse of complex MCMC algorithms for partially observed epidemic models.
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Diffeomorphic registration distances for Bayesian calibration of infinite-dimensional computer models
LDDMM distances enable interpretable Bayesian calibration and posterior prediction for infinite-dimensional computer model outputs.