The Discrete Infinite Logistic Normal Distribution

We present the discrete infinite logistic normal distribution (DILN), a Bayesian nonparametric prior for mixed membership models. DILN is a generalization of the hierarchical Dirichlet process (HDP) that models correlation structure between the weights of the atoms at the group level. We derive a representation of DILN as a normalized collection of gamma-distributed random variables, and study its statistical properties. We consider applications to topic modeling and derive a variational inference algorithm for approximate posterior inference. We study the empirical performance of the DILN topic model on four corpora, comparing performance with the HDP and the correlated topic model (CTM). To deal with large-scale data sets, we also develop an online inference algorithm for DILN and compare with online HDP and online LDA on the Nature magazine, which contains approximately 350,000 articles.Comment: This paper will appear in Bayesian Analysis. A shorter version of this paper appeared at AISTATS 2011, Fort Lauderdale, FL, US

An Algorithm for Evolving Protocol Constraints

Centre for Intelligent Systems and their ApplicationsWe present an investigation into the design of an evolutionary mechanism for multiagent protocol constraint optimisation. Starting with a review of common population based mechanisms we discuss the properties of the mechanisms used by these search methods. We derive a novel algorithm for optimisation of vectors of real numbers and empirically validate the efficacy of the design by comparing against well known results from the literature. We discuss the application of an optimiser to a novel problem and remark upon the relevance of the no free lunch theorem. We show the relative performance of the optimiser is strong and publish details of a new best result for the Keane optimisation problem. We apply the final algorithm to the multi-agent protocol optimisation problem and show the design process was successful