Related papers: Sampling from a Bayesian Menu

Discussion of "Bayesian Models and Methods in Public Policy and Government Settings" by S. E. Fienberg [arXiv:1108.2177]

Rejoinder of "Bayesian Models and Methods in Public Policy and Government Settings" by S. E. Fienberg [arXiv:1108.2177]

Fienberg convincingly demonstrates that Bayesian models and methods represent a powerful approach to squeezing illumination from data in public policy settings. However, no school of inference is without its weaknesses, and, in the face of…

Starting with the neo-Bayesian revival of the 1950s, many statisticians argued that it was inappropriate to use Bayesian methods, and in particular subjective Bayesian methods in governmental and public policy settings because of their…

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

Bayesian analysis is increasingly popular for use in social science and other application areas where the data are observations from an informative sample. An informative sampling design leads to inclusion probabilities that are correlated…

Reply to the "Discussion of "Estimating the Distribution of Dietary Consumption Patterns" by Raymond J. Carroll [arXiv:1405.4667]" by Stephen E. Fienberg and Rebecca C. Steorts [arXiv:1403.0566].

This paper describes a Bayesian method for learning causal networks using samples that were selected in a non-random manner from a population of interest. Examples of data obtained by non-random sampling include convenience samples and…

In the case of informative sampling the sampling scheme explicitly or implicitly depends on the response variable. As a result, the sample distribution of response variable can- not be used for making inference about the population. In this…

Bayesian estimation is increasingly popular for performing model based inference to support policymaking. These data are often collected from surveys under informative sampling designs where subject inclusion probabilities are designed to…

This article attempts to offer some perspectives on Bayesian inference for finite population quantities when the units in the population are assumed to exhibit complex dependencies. Beginning with an overview of Bayesian hierarchical…

Discussion of "Bayesian Model Selection Based on Proper Scoring Rules" by Dawid and Musio [arXiv:1409.5291].

Discussion of "Bayesian Model Selection Based on Proper Scoring Rules" by Dawid and Musio [arXiv:1409.5291].

Comment on ``Microarrays, Empirical Bayes and the Two-Groups Model'' [arXiv:0808.0572]

Comment on ``Microarrays, Empirical Bayes and the Two-Groups Model'' [arXiv:0808.0572]

Comment on ``Microarrays, Empirical Bayes and the Two-Group Model'' [arXiv:0808.0572]

This report summarises the outcomes of a systematic literature search to identify Bayesian network models used to support decision making in healthcare. After describing the search methodology, the selected research papers are briefly…

In this manuscript, we discuss the substantial importance of Bayesian reasoning in Social Science research. Particularly, we focus on foundational elements to fit models under the Bayesian paradigm. We aim to offer a frame of reference for…

This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal method for summarising uncertainty and making estimates and predictions using probability statements conditional on observed data and an…

Invited Discussion of "A Unified Framework for De-Duplication and Population Size Estimation", published in Bayesian Analysis. My discussion focuses on two main themes: Providing a more nuanced picture of the costs and benefits of joint…