A reversible infinite HMM using normalised random measures

We present a nonparametric prior over reversible Markov chains. We use completely random measures, specifically gamma processes, to construct a countably infinite graph with weighted edges. By enforcing symmetry to make the edges undirected we define a prior over random walks on graphs that results in a reversible Markov chain. The resulting prior over infinite transition matrices is closely related to the hierarchical Dirichlet process but enforces reversibility. A reinforcement scheme has recently been proposed with similar properties, but the de Finetti measure is not well characterised. We take the alternative approach of explicitly constructing the mixing measure, which allows more straightforward and efficient inference at the cost of no longer having a closed form predictive distribution. We use our process to construct a reversible infinite HMM which we apply to two real datasets, one from epigenomics and one ion channel recording.Comment: 9 pages, 6 figure

Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression

We propose a general algorithm for approximating nonstandard Bayesian posterior distributions. The algorithm minimizes the Kullback-Leibler divergence of an approximating distribution to the intractable posterior distribution. Our method can be used to approximate any posterior distribution, provided that it is given in closed form up to the proportionality constant. The approximation can be any distribution in the exponential family or any mixture of such distributions, which means that it can be made arbitrarily precise. Several examples illustrate the speed and accuracy of our approximation method in practice

An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process

Stochastic variational inference (SVI) is emerging as the most promising candidate for scaling inference in Bayesian probabilistic models to large datasets. However, the performance of these methods has been assessed primarily in the context of Bayesian topic models, particularly latent Dirichlet allocation (LDA). Deriving several new algorithms, and using synthetic, image and genomic datasets, we investigate whether the understanding gleaned from LDA applies in the setting of sparse latent factor models, specifically beta process factor analysis (BPFA). We demonstrate that the big picture is consistent: using Gibbs sampling within SVI to maintain certain posterior dependencies is extremely effective. However, we find that different posterior dependencies are important in BPFA relative to LDA. Particularly, approximations able to model intra-local variable dependence perform best.Comment: ICML, 12 pages. Volume 37: Proceedings of The 32nd International Conference on Machine Learning, 201

Modified Early Warning Scores (MEWS) to support ambulance clinicians’ decisions to transport or treat at home

Introduction Modified Early Warning Scores (MEWS), calculated from patients’ vital signs, are used in hospital to identify patients who may benefit from admission or intensive care: higher MEWS indicates greater clinical risk. We aimed to evaluate MEWS to support paramedics’ decisions to transport patients to hospital or treat and leave them at home. Methods We used an interrupted time series design. We trained 19 volunteer paramedics to use MEWS to support decisions to transport or treat and leave at home. We used linear regression to evaluate differences in weekly transportation rates (percentage of patients attended and transported to hospital) and revisit rates (percentage of patients attended, treated at home and subsequently revisited within 7 days), comparing trends in rates 17 weeks prior (pre-MEWS) and 17 weeks post implementation of MEWS. Auto-calculated scores retrospectively applied to all data provided pre-MEWS and were compared with paramedic calculated scores post-MEWS. Results Of the 4140 patients attended, 2208 were excluded owing to missing values (n=1897), recording errors (n=21) or excluded clinical complaints (n=290). From the remaining data (n=1932) there were no significant differences in transportation rates (pre=55±6%; post=63±11%) by catering for the existing increasing trends where the confidence intervals of the regression slopes overlap (pre=0.15; 95%CI -0.51 to 0.80 vs. post=0.54; -0.58 to 1.65). Similarly, there were no significant difference in revisit rates (pre=4±4%; post=2±4%) catering for the similar trends (pre=-0.13; -0.53 to 0.27 vs. post=0.08; -0.33 to 0.49). Paramedic scores were incorrect 39% of the time (n=622). Conclusion MEWS had a minimal effect on transportation or revisit rates. Scores were frequently not calculated or recorded, or incorrectly calculated. Opportunities for ongoing training, clinical support and feedback were limited. A larger study, ensuring adequate ongoing support, is recommended before implementing MEWS on a wider scale

Doping of a high calcium oxide metaphosphate glass with titanium dioxide

This study investigates the effect of doping a high calcium oxide containing metaphosphate glass series (CaO)(40)(Na2O)(10)(P2O5)(50) with TiO2 (1, 3, and 5 mol%). TiO2 incorporation increased the density and glass transition temperature while reduced the degradation rate (5 mol% in particular) by twofold compared with (CaO)30 system reported previously. This has been confirmed by ion release and the minimal pH changes. TiP2O7, NaCa(PO3)(3) and CaP2O6 phases were detected for all TiO2-containing ceramics. XPS showed that the surface is composed of Ca, h, and Ti. Ti was recognized mainly as TiO2, but its total amount was lower than theoretical values. P-31 magic angle spinning (MAS) NMR showed a downfield shift of the P-31 lineshape with increasing TiO2, interpreted as an effect of the titanium cation rather than an increase in the phosphate network connectivity. FTIR showed that incorporation of TiO2 increased the strength of the phosphate chains, and the O/P ratio while introducing more Q(1) units into the structure at the expense of the Q(2) units. There were no differences, however, in surface topography roughness and free energies between these glasses. These results suggested that TiO2 and CaO were acting synergistically in producing glasses with controllable bulk and structural propertie