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On the safe use of prior densities for Bayesian model selection
Furthermore, when the prior is improper, the marginal likelihood of the corresponding model is undetermined.
Automatic tempered posterior distributions for Bayesian inversion problems
More specifically, we consider a Bayesian analysis for the variables of interest (i. e., the parameters of the model to invert), whereas we employ a maximum likelihood approach for the estimation of the noise power.
MCMC-driven importance samplers
Monte Carlo sampling methods are the standard procedure for approximating complicated integrals of multidimensional posterior distributions in Bayesian inference.
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