Phylogenetic mixtures on a single tree can mimic a tree of another topology

Phylogenetic mixtures model the inhomogeneous molecular evolution commonly observed in data. The performance of phylogenetic reconstruction methods where the underlying data is generated by a mixture model has stimulated considerable recent debate. Much of the controversy stems from simulations of mixture model data on a given tree topology for which reconstruction algorithms output a tree of a different topology; these findings were held up to show the shortcomings of particular tree reconstruction methods. In so doing, the underlying assumption was that mixture model data on one topology can be distinguished from data evolved on an unmixed tree of another topology given enough data and the ``correct'' method. Here we show that this assumption can be false. For biologists our results imply that, for example, the combined data from two genes whose phylogenetic trees differ only in terms of branch lengths can perfectly fit a tree of a different topology

A wireless ultrasonic NDT sensor system

Ultrasonic condition monitoring technologies have been traditionally utilized in industrial and construction environments where structural integrity is of concern. Such techniques include active systems with either single or multiple transmit-receiver combinations used to obtain defect positioning and magnitude. Active sensors are implemented in two ways; in a thickness operation mode, or as an area-mapping tool operating over longer distances. In addition, passive ultrasonic receivers can be employed to detect and record acoustic emission activity. Existing equipment requires cabling for such systems leading to expensive, complicated installations. This work describes the development and operation of a system that combines these existing ultrasonic technologies with modern wireless techniques within a miniaturized, battery-operated design. A completely wireless sensor has been designed that can independently record and analyze ultrasonic signals. Integrated into the sensor are custom ultrasonic transducers, associated analogue drive and receive electronics, and a Texas Instruments Digital Signal Processor (DSP) used to both control the system and implement the signal processing routines. BlueTooth wireless communication is used for connection to a central observation station, from where network operation can be controlled. Extending battery life is of prime importance and the device employs several strategies to do this. Low voltage transducer excitation suffers from poor signal-to-noise ratios, which can be enhanced by signal processing routines implemented on the DSP. Routines investigated include averaging, digital filtering and pulse compression

On the variational distance of two trees

A widely studied model for generating sequences is to ``evolve'' them on a tree according to a symmetric Markov process. We prove that model trees tend to be maximally ``far apart'' in terms of variational distance.Comment: Published at http://dx.doi.org/10.1214/105051606000000196 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Non-Gaussian dynamic Bayesian modelling for panel data

A first order autoregressive non-Gaussian model for analysing panel data is proposed. The main feature is that the model is able to accommodate fat tails and also skewness, thus allowing for outliers and asymmetries. The modelling approach is to gain sufficient flexibility, without sacrificing interpretability and computational ease. The model incorporates individual effects and we pay specific attention to the elicitation of the prior. As the prior structure chosen is not proper, we derive conditions for the existence of the posterior. By considering a model with individual dynamic parameters we are also able to formally test whether the dynamic behaviour is common to all units in the panel. The methodology is illustrated with two applications involving earnings data and one on growth of countries.autoregressive modelling; growth convergence; individual effects; labour earnings; prior elicitation; posterior existence; skewed distributions

Model-based Clustering of non-Gaussian Panel Data

In this paper we propose a model-based method to cluster units within a panel. The underlying model is autoregressive and non-Gaussian, allowing for both skewness and fat tails, and the units are clustered according to their dynamic behaviour and equilibrium level. Inference is addressed from a Bayesian perspective and model comparison is conducted using the formal tool of Bayes factors. Particular attention is paid to prior elicitation and posterior propriety. We suggest priors that require little subjective input from the user and possess hierarchical structures that enhance the robustness of the inference. Two examples illustrate the methodology: one analyses economic growth of OECD countries and the second one investigates employment growth of Spanish manufacturing firmsautoregressive modelling; employment growth; GDP growth convergence; hierarchical prior; model comparison; posterior propriety; skewness