Background: Although testing for simultaneous divergence (vicariance) across different population-pairs that span the same barrier to gene flow is of central importance to evoluti...
Michael J. Hickerson, Eli Stahl, Naoki Takebayashi
Variational methods for approximate inference in machine learning often adapt a parametric probability distribution to optimize a given objective function. This view is especially ...
Antti Honkela, Matti Tornio, Tapani Raiko, Juha Ka...
In this article we address the issue of denoising photon-limited image data by deriving new and efficient multivariate Bayesian estimators that approximate the conditional expecta...
We consider reinforcement learning as solving a Markov decision process with unknown transition distribution. Based on interaction with the environment, an estimate of the transit...
In this paper, an anisotropic total variation (ATV) minimization is combined with the new adaptive tetrolet transform for discontinuity-preserving image processing. In order to su...