1 IBM Research Collaboratory for Life Sciences-Melbourne, Carlton, Australia, {reumann; John.Wagner; mattdton; stevemoore@au1.ibm.com} 2 Dept. Microbiology and Immunology, University of Melbourne, Carlton, Australia, {kholt; tstinear, sjturner}@unimelb.edu.au 3 Walter and Eliza Hall Institute in Melbourne, Australia, inouye@wehi.edu.au 4 Dept. Computer Science and Software Engineering, University of Melbourne, Carlton, Australia, {bwgoudey; gabraham}@csse.unimelb.edu.au; {qwan; adrianrp; jz}@unimelb.edu.au 5 National ICT Australia, Victoria Research Laboratories, Carlton, Australia, {Fan.Shi; adam.kowalczyk@nicta.com.au} 6 Victorian Life Sciences Computation Initiative, Carlton, Australia, {aisaac; bjpope@unimelb.edu.au} 7 Dept. of Medicine, University of Melbourne, Carlton, Australia {butz; slavep; obrientj@unimelb.edu.au 8 Deakin University, Science and Technology pcc@deakin.edu.au Howard Florey Institute, Carlton, Australia, Judith.field@florey.edu.au 10 Dept. of Pathology, University of Melbourne, msouthey@unimelb.edu.au 11 Peter MacCullum Cancer Center, Melbourne, David.Bowtell@petermac.org 12 Melbourne School of Population Health, University of Melbourne, Carlton, Australia

This paper examines the problem of simultaneously testing many independent multiple hypotheses within the minimum encoding framework. We introduce an efficient coding scheme for nominating the accepted hypotheses in addition to compressing the data given these hypotheses. This formulation reveals an interesting connection between multiple hypothesis testing and mixture modelling with the class labels corresponding to the accepted hypotheses in each test. An advantage of the resulting method is that it provides a posterior distribution over the space of tested hypotheses which may be easily integrated into decision theoretic post-testing analysis.

In this note expressions are derived that allow computation of the Kullback-Leibler (K-L) divergence between two first-order Gaussian moving average models in O n(1) time as the sample size n ¿ ¿. These expressions can also be used to evaluate the exact Fisher information matrix in On(1) time, and provide a basis for an asymptotic expression of the K-L divergence.

This paper describes a probabilistic mechanism for the interpretation of sentence sequences developed for a spoken dialogue system mounted on a robotic agent. The mechanism receives as input a sequence of sentences, and produces an interpretation which integrates the interpretations of individual sentences. For our evaluation, we collected a corpus of hypothetical requests to a robot. Our mechanism exhibits good performance for sentence pairs, but requires further improvements for sentence sequences.

This paper considers the problem of constructing information theoretic universal models for data distributed according to the exponential distribution. The universal models examined include the sequential normalized maximum likelihood (SNML) code, conditional normalized maximum likelihood (CNML) code, the minimum message length (MML) code, and the Bayes mixture code (BMC). The CNML code yields a codelength identical to the Bayesian mixture code, and within O(1) of the MML codelength, with suitable data driven priors.