Search Results for author:
Found 2 papers, 0 papers with code
Likelihood-Free Gaussian Process for Regression
In this paper, we propose a novel framework called the likelihood-free Gaussian process (LFGP), which allows representation of the posterior distributions of interest parameters for scalable problems without directly setting their likelihood functions.
Learning Bayesian Networks with Annealing Machine
However, the bit capacity of an annealing machine is currently limited.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
