Search Results for author:
Found 4 papers, 3 papers with code
Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations
Bayesian optimization (BO) is a global optimization strategy designed to find the minimum of an expensive black-box function, typically defined on a compact subset of $\mathcal{R}^d$, by using a Gaussian process (GP) as a surrogate model for the objective.
A Bayesian length-based population dynamics model for northern shrimp (Pandalus Borealis)
We introduce a fully length-based Bayesian model for the population dynamics of northern shrimp (Pandalus Borealis).
Applications
Bayesian Modeling with Gaussian Processes using the GPstuff Toolbox
The prior over functions is defined implicitly by the mean and covariance function, which determine the smoothness and variability of the function.
Gaussian process regression with Student-t likelihood
In this work, we discuss the properties of a Gaussian process regression model with the Student-t likelihood and utilize the Laplace approximation for approximate inference.
