Bayesian Learning and Predictability in a Stochastic Nonlinear Dynamical Model

Bayesian inference methods are applied within a Bayesian hierarchical modelling framework to the problems of joint state and parameter estimation, and of state forecasting. We explore and demonstrate the ideas in the context of a simple nonlinear marine biogeochemical model. A novel approach is proposed to the formulation of the stochastic process model, in which ecophysiological properties of plankton communities are represented by autoregressive stochastic processes. This approach captures the effects of changes in plankton communities over time, and it allows the incorporation of literature metadata on individual species into prior distributions for process model parameters. The approach is applied to a case study at Ocean Station Papa, using Particle Markov chain Monte Carlo computational techniques. The results suggest that, by drawing on objective prior information, it is possible to extract useful information about model state and a subset of parameters, and even to make useful long-term forecasts, based on sparse and noisy observations

Streaming, Distributed Variational Inference for Bayesian Nonparametrics

This paper presents a methodology for creating streaming, distributed inference algorithms for Bayesian nonparametric (BNP) models. In the proposed framework, processing nodes receive a sequence of data minibatches, compute a variational posterior for each, and make asynchronous streaming updates to a central model. In contrast to previous algorithms, the proposed framework is truly streaming, distributed, asynchronous, learning-rate-free, and truncation-free. The key challenge in developing the framework, arising from the fact that BNP models do not impose an inherent ordering on their components, is finding the correspondence between minibatch and central BNP posterior components before performing each update. To address this, the paper develops a combinatorial optimization problem over component correspondences, and provides an efficient solution technique. The paper concludes with an application of the methodology to the DP mixture model, with experimental results demonstrating its practical scalability and performance.Comment: This paper was presented at NIPS 2015. Please use the following BibTeX citation: @inproceedings{Campbell15_NIPS, Author = {Trevor Campbell and Julian Straub and John W. {Fisher III} and Jonathan P. How}, Title = {Streaming, Distributed Variational Inference for Bayesian Nonparametrics}, Booktitle = {Advances in Neural Information Processing Systems (NIPS)}, Year = {2015}

Gas phase absorption of C702+ below 10 K: astronomical implications

The electronic spectrum of the fullerene dication C702+ has been measured in the gas phase at low temperature in a cryogenic radiofrequency ion trap. The spectrum consists of a strong origin band at 7030 Å and two weaker features to higher energy. The bands have FWHMs of 35 Å indicating an excited state lifetime on the order of one-tenth of a picosecond. Absorption cross-section measurements yield (2 ± 1) × 10−15 cm2 at 7030 Å. These results are used to predict the depth of diffuse interstellar bands (DIBs) due to the absorption by C702+. At an assumed column density of 2 × 1012 cm−2 the attenuation of starlight at 7030 Å is around 0.4% and thus the detection of such a shallow and broad interstellar band would be difficult. The electronic spectrum of C602+ shows no absorptions in the visible. Below 4000 Å the spectra of C60, C60+ and C602+ are similar. The large intrinsic FWHM of the features in this region, ~200 Å for the band near 3250 Å, make them unsuitable for DIB detection

Factors Affecting Lateral Stability and Controllability

The effects on dynamic lateral stability and controllability of some of the important aerodynamic and mass characteristics are discussed and methods are presented for estimating the various stability parameters to be used in the calculation of the dynamic lateral stability of airplanes with swept and low-aspect-ratio wings