We introduce an adaptive output-sensitive Metropolis-Hastings algorithm for probabilistic models expressed as programs, Adaptive Lightweight Metropolis-Hastings (AdLMH). The algorithm extends Lightweight Metropolis-Hastings (LMH) by adjusting the probabilities of proposing random variables for modification to improve convergence of the program output. We show that AdLMH converges to the correct...

In many applications common in testing for convergence the number of cross-sectional units is large and the number of time periods are few. In these situations tests which are founded upon an omnibus null hypothesis are characterised by a number of problems. In this paper we consider a broad class of tests of convergence based on multivariate time series and panel data methodologies, and track ...

Training classifiers on large databases is computationally demanding. It is desirable to develop efficient procedures for a reliable prediction of a classifier's suitability for implementing a given task, so that resources can be assigned to the most promising candidates or freed for exploring new classifier candidates. We propose such a practical and principled predictive method. Practical bec...

Fridy and Miller have given a characterization of statistical convergence for bounded single sequence, by matrix summability methods. The analogues of the some of the results for double sequences were proved by H.I. Miller and Leila Miller-Van Wieren. In this paper we define the statistical convergence of double sequences in a non-trivially valued non-archimedean field K and prove a theorem on ...