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Found 6 papers, 0 papers with code
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.
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning
This survey gives an overview of Monte Carlo methodologies using surrogate models, for dealing with densities which are intractable, costly, and/or noisy.
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.
Deep Importance Sampling based on Regression for Model Inversion and Emulation
For this reason, several adaptive importance sampling (AIS) schemes have been proposed in the literature.
Adaptive quadrature schemes for Bayesian inference via active learning
For the Gaussian case, we also provide a novel procedure for fitting the bandwidth parameter, in order to build a suitable emulator of a density function.
