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In Bayesian statistics, one's prior beliefs about underlying model parameters are revised with the information content of observed data from which, using Bayes' rule, a posterior belief is obtained. A non-trivial example taken from the…

High Energy Physics - Phenomenology · Physics 2007-05-23 J. Charles , A. Hocker , H. Lacker , F. R. Le Diberder , S. T'Jampens

Motivated by a recent paper that compares the results of the analysis of the CKM angle alpha in the frequentist and in the Bayesian approaches, we have reconsidered the information on the hadronic amplitudes, which helps constraining the…

In contrast to previous analyses, we demonstrate a Bayesian approach to the estimation of the CKM phase $\alpha$ that is invariant to parameterization. We also show that in addition to {\em computing} the marginal posterior in a Bayesian…

High Energy Physics - Phenomenology · Physics 2009-03-31 Robin D. Morris , Johann Cohen-Tanugi

We present results of a unitary triangle fit based on the scan method. This frequentist approach employs Gaussian uncertainties for experimental quantities, but makes no arbitrary assumptions about the distribution of theoretical errors.…

High Energy Physics - Experiment · Physics 2015-06-12 Gerald Eigen , Gregory Dubois-Felsmann , David G. Hitlin , Frank C. Porter

The extraction of the weak phase $\alpha$ from $B\to\pi\pi$ decays has been controversial from a statistical point of view, as the frequentist vs. bayesian confrontation shows. We analyse several relevant questions which have not deserved…

High Energy Physics - Phenomenology · Physics 2007-05-23 Francisco J. Botella , Miguel Nebot

Bayesian and frequentist methods differ in many aspects, but share some basic optimality properties. In practice, there are situations in which one of the methods is more preferred by some criteria. We consider the case of inference about a…

Statistics Theory · Mathematics 2009-08-25 Ao Yuan

We discuss the determination of the CKM angle $\alpha$ using the non-leptonic two-body decays $B\to \pi\pi$, $B\to \rho\rho$ and $B\to \rho\pi$ using the latest data available. We illustrate the methods used in each case and extract the…

High Energy Physics - Phenomenology · Physics 2017-09-08 J. Charles , O. Deschamps , S. Descotes-Genon , V. Niess

We marshall the arguments for preferring Bayesian hypothesis testing and confidence sets to frequentist ones. We define admissible solutions to inference problems, noting that Bayesian solutions are admissible. We give seven weaker…

Statistics Theory · Mathematics 2024-05-22 Roger Sewell

We perform extensive Monte Carlo simulations to systematically compare the frequentist and Bayesian treatments of the Lomb--Scargle periodogram. The goal is to investigate whether the Bayesian period search is advantageous over the…

Instrumentation and Methods for Astrophysics · Physics 2025-01-22 Roman V. Baluev

When combining apparently inconsistent experimental results, one often implements errors on errors. The Particle Data Group's phenomenological prescription offers a practical solution but lacks a firm theoretical foundation. To address…

High Energy Physics - Phenomenology · Physics 2025-08-22 Satoshi Mishima , Kin-ya Oda

We propose a new method to extract the CKM phase angle \gamma (\equiv \phi_3) from the isospin analysis in B \to K \pi decays. Unlike previously proposed methods, we do not employ flavor SU(3) symmetry, so that this method is free from the…

High Energy Physics - Phenomenology · Physics 2009-01-01 C. S. Kim , Sechul Oh , Yeo Woong Yoon

Piecewise constant priors are routinely used in the Bayesian Cox proportional hazards model for survival analysis. Despite its popularity, large sample properties of this Bayesian method are not yet well understood. This work provides a…

Statistics Theory · Mathematics 2023-06-16 Bo Y. -C. Ning , Ismaël Castillo

Rigorously quantifying the information in high contrast imaging data is important for informing follow-up strategies to confirm the substellar nature of a point source, constraining theoretical models of planet-disk interactions, and…

We compare the accuracy, precision and reliability of different methods for estimating key system parameters for two-level systems subject to Hamiltonian evolution and decoherence. It is demonstrated that the use of Bayesian modelling and…

Quantum Physics · Physics 2019-10-15 Sophie Schirmer , Frank Langbein

The simultaneous estimation of multiple unknown parameters lies at heart of a broad class of important problems across science and technology. Currently, the state-of-the-art performance in the such problems is achieved by nonparametric…

Statistics Theory · Mathematics 2023-05-30 Alton Barbehenn , Sihai Dave Zhao

Increasingly complex applications involve large datasets in combination with non-linear and high dimensional mathematical models. In this context, statistical inference is a challenging issue that calls for pragmatic approaches that take…

Data Analysis, Statistics and Probability · Physics 2013-01-31 Andreas Raue , Clemens Kreutz , Fabian Joachim Theis , Jens Timmer

In this work, the uncertainty associated with the finite element discretization error is modeled following the Bayesian paradigm. First, a continuous formulation is derived, where a Gaussian process prior over the solution space is updated…

Numerical Analysis · Mathematics 2024-03-11 Anne Poot , Pierre Kerfriden , Iuri Rocha , Frans van der Meer

Taking into account the recent measurements of the branching ratios and CP asymmetries in the $B \to \pi\pi$ decays by the BABAR and BELLE collaborations, an amplitude analysis based on the isospin symmetry is reported. These data allow to…

High Energy Physics - Phenomenology · Physics 2017-08-23 A. Ya. Parkhomenko

Bayesian approaches for handling covariate measurement error are well established, and yet arguably are still relatively little used by researchers. For some this is likely due to unfamiliarity or disagreement with the Bayesian inferential…

Methodology · Statistics 2017-08-01 Jonathan W. Bartlett , Ruth H. Keogh

Bayesian methods are actively used for parameter identification and uncertainty quantification when solving nonlinear inverse problems with random noise. However, there are only few theoretical results justifying the Bayesian approach.…

Statistics Theory · Mathematics 2020-02-04 Vladimir Spokoiny
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