On quantifying expert opinion about multinomial models that contain covariates

This paper addresses the task of forming a prior distribution to represent expert opinion about a multinomial model that contains covariates. The task has not previously been addressed. We suppose the sampling model is a multinomial logistic regression and represent expert opinion about the regression coefficients by a multivariate normal distribution. This logistic-normal model gives a flexible prior distribution that can capture a broad variety of expert opinion. The challenge is to (i) find meaningful assessment tasks that an expert can perform and which should yield appropriate information to determine the values of parameters in the prior distribution, and (ii) develop theory for determining the parameter values from the assessments. A method is proposed that meets this challenge. The method is implemented in interactive user-friendly software that is freely available. It provides a graphical interface that the expert uses to assess quartiles of sets of proportions and the method determines a mean vector and a positive-definite covariance matrix to represent the expert's opinions. The chosen assessment tasks yield parameter values that satisfy the usual laws of probability without the expert being aware of the constraints this imposes. Special attention is given to feedback that encourages the expert to consider his/her opinions from a different perspective. The method is illustrated in an example that shows its viability and usefulness

Gaussian processes for switching regimes

It has been shown that Gaussian processes are a competitive tool for nonparametric regression and classification. Furthermore they are equivalent to neural networks in the limit of an infinite number of neurons. Here we show that the versatility of Gaussian processes at defining different textural characteristics can be used to recognise different regimes in a signal switching between different sources

Effect of Short Term Exercise and High Fat Diet on Skeletal Muscle miR133a

Micro RNAs (miR) are small non-coding RNA that regulate gene expression at the post-transcriptional level. miR133a is abundant in cardiac and skeletal muscle. In skeletal muscle, miR133a is best known for its regulatory role in myogenesis and differentiation. Nie (2016) found that muscle miR133a expression increased after acute exercise and with 12w of treadmill exercise training in mice. Knockdown of miR133a in transgenic mice resulted in blunted skeletal muscle mitochondrial biogenesis and function in response to exercise training (Nie, 2016) suggesting a role for miR133a in regulating the normal skeletal muscle metabolic adaptive response to exercise. Among other miR, skeletal muscle miR133a is reported as downregulated in insulin-resistant muscle. Insulin resistance in mice fed a high-fat diet is detectable after 3 days on diet (Lee, 2011). In this study, voluntary, rather than forced, exercise was employed to test whether miR133a expression is regulated early in the adoption of increased daily physical activity

A novel approach to light-front perturbation theory

We suggest a possible algorithm to calculate one-loop n-point functions within a variant of light-front perturbation theory. The key ingredients are the covariant Passarino-Veltman scheme and a surprising integration formula that localises Feynman integrals at vanishing longitudinal momentum. The resulting expressions are generalisations of Weinberg's infinite-momentum results and are manifestly Lorentz invariant. For n = 2 and 3 we explicitly show how to relate those to light-front integrals with standard energy denominators. All expressions are rendered finite by means of transverse dimensional regularisation.Comment: 10 pages, 5 figure

Fast methods for training Gaussian processes on large data sets

Gaussian process regression (GPR) is a non-parametric Bayesian technique for interpolating or fitting data. The main barrier to further uptake of this powerful tool rests in the computational costs associated with the matrices which arise when dealing with large data sets. Here, we derive some simple results which we have found useful for speeding up the learning stage in the GPR algorithm, and especially for performing Bayesian model comparison between different covariance functions. We apply our techniques to both synthetic and real data and quantify the speed-up relative to using nested sampling to numerically evaluate model evidences.Comment: Fixed missing reference

Reference priors for high energy physics

Bayesian inferences in high energy physics often use uniform prior distributions for parameters about which little or no information is available before data are collected. The resulting posterior distributions are therefore sensitive to the choice of parametrization for the problem and may even be improper if this choice is not carefully considered. Here we describe an extensively tested methodology, known as reference analysis, which allows one to construct parametrization-invariant priors that embody the notion of minimal informativeness in a mathematically well-defined sense. We apply this methodology to general cross section measurements and show that it yields sensible results. A recent measurement of the single top quark cross section illustrates the relevant techniques in a realistic situation

Statistical methods in cosmology

The advent of large data-set in cosmology has meant that in the past 10 or 20 years our knowledge and understanding of the Universe has changed not only quantitatively but also, and most importantly, qualitatively. Cosmologists rely on data where a host of useful information is enclosed, but is encoded in a non-trivial way. The challenges in extracting this information must be overcome to make the most of a large experimental effort. Even after having converged to a standard cosmological model (the LCDM model) we should keep in mind that this model is described by 10 or more physical parameters and if we want to study deviations from it, the number of parameters is even larger. Dealing with such a high dimensional parameter space and finding parameters constraints is a challenge on itself. Cosmologists want to be able to compare and combine different data sets both for testing for possible disagreements (which could indicate new physics) and for improving parameter determinations. Finally, cosmologists in many cases want to find out, before actually doing the experiment, how much one would be able to learn from it. For all these reasons, sophisiticated statistical techniques are being employed in cosmology, and it has become crucial to know some statistical background to understand recent literature in the field. I will introduce some statistical tools that any cosmologist should know about in order to be able to understand recently published results from the analysis of cosmological data sets. I will not present a complete and rigorous introduction to statistics as there are several good books which are reported in the references. The reader should refer to those.Comment: 31, pages, 6 figures, notes from 2nd Trans-Regio Winter school in Passo del Tonale. To appear in Lectures Notes in Physics, "Lectures on cosmology: Accelerated expansion of the universe" Feb 201

Next generation organofluorine containing blockbuster drugs

Funding: the National Natural Science Foundation of China (No. 21761132021), the Hungarian Research Foundation (NKFIH No. K 119282), and Ministry of Human Capacities, Hungary grant 20391-3/2018/FEKUSTRAT. The Qinlan Project of Jiangsu Province, and IKERBASQUE, the Basque Foundation for Science are also acknowledged.The role of organo-fluorine compounds in modern health, food and energy related industries is widely-appreciated. The unique properties that fluorine imparts to organic molecules, stemming from its high electronegativity and stability when bound to carbon, finds it increasing being used in the development of new bioactivities. Around 25% of the current blockbuster drugs contain fluorine and this number is increasing to well above 30% for recent FDA approvals. In this Review we highlight a selection of the most successful organo-fluorine drugs, that have achieved blockbuster status, namely, sitagliptin (diabetes), sofosbuvir (hepatitis C), emtricitabine (HIV), glecaprevir/pibrentasvir (hepatitis C), elvitegravir (HIV), dolutegravir (HIV), bictegravir (HIV), efavirenz (HIV), enzalutamide (prostate cancer), aubagio (immunomodulatory) and paliperidone palmitate (schizophrenia). For each compound we discuss their discovery, their relevant disease state and how they are made, emphasizing the source of fluorine-containing moieties, and where known, their mode of action.PostprintPeer reviewe