Related papers: Bayesian inference of $W$-boson mass
We present a search for standard model Higgs boson to WW(*) production in dilepton plus missing transverse energy final states in data collected by the CDF II detector corresponding to 4.8/fb of integrated luminosity. To maximize…
We present a detailed analysis of epsilon'/epsilon within the Standard Model, taking into account the strong enhancement through final-state interactions identified in refs. [1] and [2]. The relevant hadronic matrix elements are fixed at…
We consider the setting of an aggregate data meta-analysis of a continuous outcome of interest. When the distribution of the outcome is skewed, it is often the case that some primary studies report the sample mean and standard deviation of…
The W-boson mass measurement recently reported by the CDF II experiment exhibits a significant deviation from both the Standard Model prediction and previous measurements. There is also a long-standing deviation between the Standard Model…
We present a measurement of the W boson mass in proton-antiproton collisions at \sqrt{s} = 1.8 TeV based on a data sample of 82 pb^-1 integrated luminosity collected by the D0 detector at the Fermilab Tevatron. We utilize e \nu events in…
A precise determination of the W boson mass is an essential test for the Standard Model of particle physics: the comparison of experimental value and theoretical prediction allows to probe the internal consistency of the electroweak sector…
At LEP2 many Standard Model predictions are tested up to centre-of-mass energies of 209 GeV. Fermion pair production cross sections and asymmetries agree well with the theoretical expectation over the entire energy range. The measurements…
Statisticians often face the choice between using probability models or a paradigm defined by minimising a loss function. Both approaches are useful and, if the loss can be re-cast into a proper probability model, there are many tools to…
In many applications there is interest in estimating the relation between a predictor and an outcome when the relation is known to be monotone or otherwise constrained due to the physical processes involved. We consider one such…
In the estimation of the causal effect under linear Structural Causal Models (SCMs), it is common practice to first identify the causal structure, estimate the probability distributions, and then calculate the causal effect. However, if the…
A measurement of the W mass and width has been performed by the DELPHI collaboration using the data collected during 1998. The data sample has an integrated luminosity of 155 pb^{-1} and an average centre-of-mass energy of 188.6 GeV.…
Following the recent update measurement of the W boson mass performed by the CDF-II experiment at Fermilab which indicates $7\sigma$ deviation from the SM prediction. As a consequence, the open question is whether there are extensions of…
Although linear regression models are fundamental tools in statistical science, the estimation results can be sensitive to outliers. While several robust methods have been proposed in frequentist frameworks, statistical inference is not…
We propose a method for estimating the posterior distribution of a standard geostatistical model. After choosing the model formulation and specifying a prior, we use normal mixture densities to approximate the posterior distribution. The…
Measuring di-Higgs production in the four-bottom channel is challenged by overwhelming QCD backgrounds and imperfect simulations. We develop a Bayesian mixture model that simultaneously infers signal and background fractions and their…
A novel method of determining the mass of the W boson in the W+W- -> lnu lnu channel is presented and applied to 667pb^-1 of data recorded at the center-of- mass energies in the range 183-207 GeV with the OPAL detector at LEP. The Measured…
When using complex Bayesian models to combine information, the checking for consistency of the information being combined is good statistical practice. Here a new method is developed for detecting prior-data conflicts in Bayesian models…
Bayesian approaches have become increasingly popular in causal inference problems due to their conceptual simplicity, excellent performance and in-built uncertainty quantification ('posterior credible sets'). We investigate Bayesian…
This paper proposes a Bayesian method for estimating the parameters of a normal distribution when only limited summary statistics (sample mean, minimum, maximum, and sample size) are available. To estimate the parameters of a normal…
In this paper we consider Bayesian estimation for the parameters of inverse Gaussian distribution. Our emphasis is on Markov Chain Monte Carlo methods. We provide complete implementation of the Gibbs sampler algorithm. Assuming an…