Search Results for author:
Found 4 papers, 0 papers with code
Fusion of Gaussian Processes Predictions with Monte Carlo Sampling
In this paper, we operate within the Bayesian paradigm, relying on Gaussian processes as our models.
CAMEO: Curiosity Augmented Metropolis for Exploratory Optimal Policies
We further show that the different policies we sample present different risk profiles, corresponding to interesting practical applications in interpretability, and represents a first step towards learning the distribution of optimal policies itself.
Optimality in Noisy Importance Sampling
In this work, we analyze the noisy importance sampling (IS), i. e., IS working with noisy evaluations of the target density.
Marginal likelihood computation for model selection and hypothesis testing: an extensive review
This is an up-to-date introduction to, and overview of, marginal likelihood computation for model selection and hypothesis testing.
