Inverse problems arise when one seeks to recover unknown parameters or functions from indirect, noisy observations via a forward model. The Bayesian framework casts this recovery as the updating of a ...
Bayesian inference relies on the computation of posterior distributions to update beliefs about model parameters in the light of observed data. Markov Chain Monte Carlo (MCMC) methods form a flexible ...
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