Modelling the evolution of distributions : an application to major league baseball

We develop Bayesian techniques for modelling the evolution of entire distributions over time and apply them to the distribution of team performance in Major League baseball for the period 1901-2000. Such models offer insight into many key issues (e.g. competitive balance) in a way that regression-based models cannot. The models involve discretizing the distribution and then modelling the evolution of the bins over time through transition probability matrices. We allow for these matrices to vary over time and across teams. We find that, with one exception, the transition probability matrices (and, hence, competitive balance) have been remarkably constant across time and over teams. The one exception is the Yankees, who have outperformed all other teams

Photoacoustic spectroscopy for estimating nutritional indices in Lepidopteran defoliators

Lymantria dispar L. and Malacosoma neustrium (L.) are the most serious defoliators of cork oak in the Mediterranean region. For this reason, information on their feeding behaviour are important in pest management. A non-destructive approach by using photoacoustic spectroscopy (PAS) combined with a partial least squares regression analyses (PLS), has been used to provide a rapid and cost-effective analysis to assess foliage chemistry and to estimate some nutritional indices of these insects

Measuring and Reducing the Cognitive Load for the End Users of Complex Systems

With the proliferation of complex computer systems, end users face a never-ending increase in the numbernof tasks, methods, inputs, passwords, usernames (and so on) when using online and standalone computerbased systems and applications. This paper examines a method and approach to measure how complex a system is to use, and how to reduce the complexity of such systems by minimising the requirement for human inputs as much as possible, in order to reduce the cognitive load for that user, or group of users. This paper addresses a study completed around using virtualised computer management systems interfacesof two well-known products AWS (Amazon Web Services), Oracle Cloud, and compares the complexity of the steps and interface for end users to a private cloud less well-known system called the IDE (Intelligent Design Engine). By using a set of derived formula, we examine how this can be applied to systems that have qualitative data feedback from the experiment process, and how to convert this effectively into quantitative data. This data is then analysed numerically using a unique approach to provide additional and meaningful results based of the original end user data

Bayesian Inference for Structural Vector Autoregressions Identified by Markov-Switching Heteroskedasticity

In this study, Bayesian inference is developed for structural vector autoregressive models in which the structural parameters are identified via Markov-switching heteroskedasticity. In such a model, restrictions that are just-identifying in the homoskedastic case, become over-identifying and can be tested. A set of parametric restrictions is derived under which the structural matrix is globally or partially identified and a Savage-Dickey density ratio is used to assess the validity of the identification conditions. The latter is facilitated by analytical derivations that make the computations fast and numerical standard errors small. As an empirical example, monetary models are compared using heteroskedasticity as an additional device for identification. The empirical results support models with money in the interest rate reaction function.Comment: Keywords: Identification Through Heteroskedasticity, Bayesian Hypotheses Assessment, Markov-switching Models, Mixture Models, Regime Chang

BRIE: transcriptome-wide splicing quantication in single cells

Abstract Single-cell RNA-seq (scRNA-seq) provides a comprehensive measurement of stochasticity in transcription, but the limitations of the technology have prevented its application to dissect variability in RNA processing events such as splicing. Here, we present BRIE (Bayesian regression for isoform estimation), a Bayesian hierarchical model that resolves these problems by learning an informative prior distribution from sequence features. We show that BRIE yields reproducible estimates of exon inclusion ratios in single cells and provides an effective tool for differential isoform quantification between scRNA-seq data sets. BRIE, therefore, expands the scope of scRNA-seq experiments to probe the stochasticity of RNA processing