BAYESIAN REASONING IN DATA ANALYSIS: A CRITICAL INTRODUCTION

By G. D'Agostini. 2003

140 citations recorded by the source corpus.

Critical review and outline of the Bayesian alternative: uncertainty in physics and the usual methods of handling it a probabilistic theory of measurement uncertainty. A Bayesian primer: subjective probability and Bayes' theorem probability distributions (a concise reminder) Bayesian inference of continuous quantities Gaussian likelihood counting experiments bypassing Bayes' theorem for routine applications Bayesian unfolding. Further comments, examples and applications: miscellanea on general issues in probability and inference combination of experimental results - a closer look asymmetric uncertainties and nonlinear propagation which priors for frontier physics? Concluding matter: conclusions and bibliography.

Subfields: History and Philosophy of Science

View Paper: https://www.semanticscholar.org/paper/1ff44a66a4f47d8ff091a9defd6c36fb002b8118

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In History and Philosophy of Science

Citations in the corpus

A determination of parton distributions with faithful uncertainty estimation (2008, methodology): “The treatment of normalization uncertainties needs some care: as is well known, including normalization uncertainties in the covariance matrix would lead to a fit that is systematically biased to lie below the data [ 66 ].”
A Monte Carlo global analysis of the Standard Model Effective Field Theory: the top quark sector (2019, background): “7) avoids the bias associated to multiplicative uncertainties, which would lead to a systematic undershooting of the best-fit values as compared to their true values [160].”
BAT - The Bayesian Analysis Toolkit (2008, methodology): “In the Bayesian approach, the quantities P (M) and P (~λ|M) are treated as probabilities (probability densities), although they are not in any sense frequency distributions and are more accurately described as degrees-of-belief [1].”
Confronting Lemaitre–Tolman–Bondi models with observational cosmology (2008, methodology): “ined data set is shown in yellow with 1-, 2-, and 3-σ contours, while the individual SNIa, BAO, and CMB data sets are shown in blue, purple, and green respectively with 1- and 2-σ contours. see Refs. [46, 47, 48, 49]. For instance, the usual method of comparing minimum χ2 per eﬀective degree of free”