Stable Radial Basis Function Selection via Mixture Modelling of the Sample Path

We consider a fully Bayesian treatment of radial basis function regression, and propose a solution to the the instability of basis selection. Indeed, when bases are selected solely according to the magnitude of their posterior inclusion probabilities, it is often the case that many bases in the same neighborhood end up getting selected leading to redundancy and ultimately inaccuracy of the representation. In this paper, we propose a straightforward solution to the problem based on post-processing the sample path yielded by the model space search technique. Specifically, we perform an a posteriori model-based clustering of the sample path via a mixture of Gaussians, and then select the points closer to the means of the Gaussians. Our solution is found to be more stable and yields a better performance on simulated and real tasks

Learning by bumping: Pathways of Dutch SMEs to foreign direct investment in Asia

This paper investigates how eleven Dutch small and medium‐sized enterprises (SMEs) transnationalised with East and Southeast Asian economies by means of establishing a foreign subsidiary. The study's aim is to elucidate how firms learned to become a transnational corporation and to gauge the relevance of the firm's external networks in the acquisition of the appropriate knowledge. The paper conceptualises SME transnationalisation as an organisational process that can be understood by theories developed in innovation studies. Through qualitative research on transnationalisation pathways, inferences are drawn on the skills and routines that are necessary to bridge institutional differences and the process by which these skills are acquired and routinised within the firm

Decision Problems in Information Theory

Constraints on entropies are considered to be the laws of information theory. Even though the pursuit of their discovery has been a central theme of research in information theory, the algorithmic aspects of constraints on entropies remain largely unexplored. Here, we initiate an investigation of decision problems about constraints on entropies by placing several different such problems into levels of the arithmetical hierarchy. We establish the following results on checking the validity over all almost-entropic functions: first, validity of a Boolean information constraint arising from a monotone Boolean formula is co-recursively enumerable; second, validity of "tight" conditional information constraints is in ???. Furthermore, under some restrictions, validity of conditional information constraints "with slack" is in ???, and validity of information inequality constraints involving max is Turing equivalent to validity of information inequality constraints (with no max involved). We also prove that the classical implication problem for conditional independence statements is co-recursively enumerable