One of the most compelling features of Gaussian process (GP) regression is its ability to provide well calibrated posterior distributions. Recent advances in inducing point methods have drastically sped up marginal likelihood and posterior mean computations, leaving posterior covariance estimation and sampling as the remaining computational bottlenecks. In this paper we address this shortcoming...

Given a pattern string of length m for the string matching problem, we design an algorithm that computes deterministic samples of a suuciently long substring of the pattern in constant time. This problem used to be the bottleneck in the pattern preprocessing for one-and two-dimensional pattern matching. The best previous time bound was O(log 2 m= log log m). We use this algorithm to obtain the ...

Let G be a graph and let P (n) denote an element from a one-parameter family of graphs, such as a path of length n, a cycle of length n, or a complete binary tree of height n. We are concerned with determining minimum dominating sets of graphs of the form G P (n). Using dynamic programming and properties of finite state spaces, we show a constant time algorithm to produce a minimum dominating s...