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Discussion of "Statistical Inference: The Big Picture" by R. E. Kass [arXiv:1106.2895]

Methodology · Statistics 2011-06-20 Hal Stern

Discussion of "Statistical Inference: The Big Picture" by R. E. Kass [arXiv:1106.2895]

Methodology · Statistics 2011-06-20 Robert McCulloch

Discussion of "Statistical Inference: The Big Picture" by R. E. Kass [arXiv:1106.2895]

Methodology · Statistics 2011-06-20 Steven N. Goodman

Opinion Dynamics lacks a theoretical basis. In this article, I propose to use a decision-theoretic framework, based on the updating of subjective probabilities, as that basis. We will see we get a basic tool for a better understanding of…

Physics and Society · Physics 2012-11-21 Andre C. R. Martins

Exposure assessment models are deterministic models derived from physical-chemical laws. In real workplace settings, chemical concentration measurements can be noisy and indirectly measured. In addition, inference on important parameters…

Applications · Statistics 2018-07-09 Nada Abdalla , Sudipto Banerjee , Gurumurthy Ramachandran , Susan Arnold

This manuscript presents an advanced framework for Bayesian learning by incorporating action and state-dependent signal variances into decision-making models. This framework is pivotal in understanding complex data-feedback loops and…

Methodology · Statistics 2023-11-29 Kaiwen Hou

Public sentiment is a direct public-centric indicator for the success of effective action planning. Despite its importance, systematic modeling of public sentiment remains untapped in previous studies. This research aims to develop a…

Social and Information Networks · Computer Science 2020-04-07 Yudi Chen , Qi Wang , Wenying Ji

Two contributions to the discussion of Fearnhead P. and D. Prangle (2012). Constructing summary statistics for approximate Bayesian computation: Semi-automatic approx- imate Bayesian computation, J. Roy. Statist. Soc. B, 74 (3).

Computation · Statistics 2012-01-10 D. J. Nott , Y. Fan , S. A. Sisson

This introduction to Bayesian statistics presents the main concepts as well as the principal reasons advocated in favour of a Bayesian modelling. We cover the various approaches to prior determination as well as the basis asymptotic…

Methodology · Statistics 2010-02-09 Christian P. Robert , Judith Rousseau

In this paper we briefly review the main methodological aspects concerned with the application of the Bayesian approach to model choice and model averaging in the context of variable selection in regression models. This includes prior…

Computation · Statistics 2016-12-08 Anabel Forte , Gonzalo Garcia-Donato , Mark Steel

Rejoinder of "Instrumental Variables: An Econometrician's Perspective" by Guido W. Imbens [arXiv:1410.0163].

Methodology · Statistics 2014-10-03 Guido Imbens

Our comment on Garc\'ia-Donato et al. (2025). "Model uncertainty and missing data: An objective Bayesian perspective" explores a further extension of the proposed methodology. Specifically, we consider the sequential setting where…

Methodology · Statistics 2025-10-01 Sebastian Arnold , Alexander Ly

In this paper the problem of learning appropriate bias for an environment of related tasks is examined from a Bayesian perspective. The environment of related tasks is shown to be naturally modelled by the concept of an {\em objective}…

Machine Learning · Computer Science 2019-11-15 Jonathan Baxter

We consider the problem of model choice for stochastic epidemic models given partial observation of a disease outbreak through time. Our main focus is on the use of Bayes factors. Although Bayes factors have appeared in the epidemic…

Computation · Statistics 2017-10-16 Muteb Alharthi , Theodore Kypraios , Philip D. O'Neill

This paper offers a comprehensive introduction to Bayesian inference, combining historical context, theoretical foundations, and core analytical examples. Beginning with Bayes' theorem and the philosophical distinctions between Bayesian and…

Methodology · Statistics 2025-12-08 Juan Sosa , Carlos A. Martínez , Danna Cruz

On some problems in the article "Efficient Likelihood Estimation in State Space Models" by Cheng-Der Fuh [Ann. Statist. 34 (2006) 2026--2068] [arXiv:math/0611376]

Statistics Theory · Mathematics 2010-03-01 Jens Ledet Jensen

These are the written discussions of the paper "Bayesian measures of model complexity and fit" by D. Spiegelhalter et al. (2002), following the discussions given at the Annual Meeting of the Royal Statistical Society in Newcastle-upon-Tyne…

Methodology · Statistics 2013-10-14 E. Moreno , F. -J. Vazquez-Polo , C. P. Robert

There is currently a renewed interest in the Bayesian predictive approach to statistics. This paper offers a review on foundational concepts and focuses on predictive modeling, which by directly reasoning on prediction, bypasses inferential…

Statistics Theory · Mathematics 2024-11-22 Sandra Fortini , Sonia Petrone

In public opinion studies, the relationships between opinions on different topics are likely to shift based on the characteristics of the respondents. Thus, understanding the complexities of public opinion requires methods that can account…

Collaborative tagging systems, such as Delicious, CiteULike, and others, allow users to annotate resources, e.g., Web pages or scientific papers, with descriptive labels called tags. The social annotations contributed by thousands of users,…

Artificial Intelligence · Computer Science 2010-05-28 Anon Plangprasopchok , Kristina Lerman