A speculative remark on holography

Holography suggests a considerable reduction of degrees of freedom in theories with gravity. However it seems to be difficult to understand how holography could be realized in a closed re--contracting universe. In this letter we claim that a scenario which achieves that goal will eliminate all spatial degrees of freedom. This would require a different concept of quantum mechanics and would imply an intriguing increase of power for the natural laws.Comment: 14 pages, a reference adde

Computing with confidence: a Bayesian approach

Bayes’ rule is introduced as a coherent strategy for multiple recomputations of classifier system output, and thus as a basis for assessing the uncertainty associated with a particular system results --- i.e. a basis for confidence in the accuracy of each computed result. We use a Markov-Chain Monte Carlo method for efficient selection of recomputations to approximate the computationally intractable elements of the Bayesian approach. The estimate of the confidence to be placed in any classification result provides a sound basis for rejection of some classification results. We present uncertainty envelopes as one way to derive these confidence estimates from the population of recomputed results. We show that a coarse SURE or UNSURE confidence rating based on a threshold of agreed classifications works well, not only pinpointing those results that are reliable but also in indicating input data problems, such as corrupted or incomplete data, or application of an inadequate classifier model

Representing classifier confidence in the safety critical domain: an illustration from mortality prediction in trauma cases

Copyright © 2007 Springer Verlag. The final publication is available at link.springer.comThis work proposes a novel approach to assessing confidence measures for software classification systems in demanding applications such as those in the safety critical domain. Our focus is the Bayesian framework for developing a model-averaged probabilistic classifier implemented using Markov chain Monte Carlo (MCMC) and where appropriate its reversible jump variant (RJ-MCMC). Within this context we suggest a new technique, building on the reject region idea, to identify areas in feature space that are associated with "unsure" classification predictions. We term such areas "uncertainty envelopes" and they are defined in terms of the full characteristics of the posterior predictive density in different regions of the feature space. We argue this is more informative than use of a traditional reject region which considers only point estimates of predictive probabilities. Results from the method we propose are illustrated on synthetic data and also usefully applied to real life safety critical systems involving medical trauma data

Experimental Comparison of Classification Uncertainty for Randomised and Bayesian Decision Tree Ensembles

Copyright © 2004 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.comBook title: Intelligent Data Engineering and Automated Learning – IDEAL 20045th International Conference on Intelligent Data Engineering and Automated Learning – IDEAL 2004, Exeter, UK. August 25-27, 2004In this paper we experimentally compare the classification uncertainty of the randomised Decision Tree (DT) ensemble technique and the Bayesian DT technique with a restarting strategy on a synthetic dataset as well as on some datasets commonly used in the machine learning community. For quantitative evaluation of classification uncertainty, we use an Uncertainty Envelope dealing with the class posterior distribution and a given confidence probability. Counting the classifier outcomes, this technique produces feasible evaluations of the classification uncertainty. Using this technique in our experiments, we found that the Bayesian DT technique is superior to the randomised DT ensemble technique

A Bayesian Methodology for Estimating Uncertainty of Decisions in Safety-Critical Systems

In: Integrated Intelligent Systems for Engineering Design (editors: Zha, X.F. and Howlett, R.J.)Frontiers in Artificial Intelligence and Applications vol. 14

A Bayesian methodology for estimating uncertainty of decisions in safety-critical systems

Published as chapter in Frontiers in Artificial Intelligence and Applications. Volume 149, IOS Press Book, 2006. Integrated Intelligent Systems for Engineering Design. Edited by Xuan F. Zha, R.J. Howlett. ISBN 978-1-58603-675-1, pp. 82-96. This version deposited in arxiv.orghttp://arxiv.org/abs/1012.0322Uncertainty of decisions in safety-critical engineering applications can be estimated on the basis of the Bayesian Markov Chain Monte Carlo (MCMC) technique of averaging over decision models. The use of decision tree (DT) models assists experts to interpret causal relations and find factors of the uncertainty. Bayesian averaging also allows experts to estimate the uncertainty accurately when a priori information on the favored structure of DTs is available. Then an expert can select a single DT model, typically the Maximum a Posteriori model, for interpretation purposes. Unfortunately, a priori information on favored structure of DTs is not always available. For this reason, we suggest a new prior on DTs for the Bayesian MCMC technique. We also suggest a new procedure of selecting a single DT and describe an application scenario. In our experiments on the Short-Term Conflict Alert data our technique outperforms the existing Bayesian techniques in predictive accuracy of the selected single DTs.Supported by a grant from the EPSRC under the Critical Systems Program, grant GR/R24357/0

New H2 collision-induced absorption and NH3 opacity and the spectra of the coolest brown dwarfs

We present new cloudy and cloudless model atmospheres for brown dwarfs using recent ab initio calculations of the line list of ammonia (NH3) and of the collision-induced absorption of molecular hydrogen (H2). We compare the new synthetic spectra with models based on an earlier description of the H2 and NH3 opacities. We find a significant improvement in fitting the nearly complete spectral energy distribution of the T7p dwarf Gliese 570D and in near infrared color-magnitude diagrams of field brown dwarfs. We apply these new models to the identification of NH3 absorption in the H band peak of very late T dwarfs and the new Y dwarfs and discuss the observed trend in the NH3-H spectral index. The new NH3 line list also allows a detailed study of the medium resolution spectrum of the T9/T10 dwarf UGPS J072227.51-054031.2 where we identify several specific features caused by NH3.Comment: 37 pages, 13 figures. Accepted for publication in the Astrophysical