Bayesian Analysis for the Social Sciences

By S. Jackman. 2009

777 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f8e78a5819eac73d929356a21e71ca7bface558b

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Citations in the corpus

Improving Transparency and Replication in Bayesian Statistics: The WAMBS-Checklist (2017, neutral): “…novice reader to, among many others: Bolstad (2007); Carlin and Louis (2009); Christensen, Johnson, Branscum, and Hanson (2010); Depaoli and Boyajian, (2014); Gelman and Hill (2007); Kaplan, 2014; Kruschke (2010); Jackman (2009); Lynch (2007); Ntzoufras (2009); or van de Schoot and Depaoli (2014).”
Observation of an exotic narrow doubly charmed tetraquark (2021, background): “• Imperfect knowledge of detector resolution model: to estimate the associated systematic uncertainty alternative resolution functions are studied, namely a symmetric variant of an Apollonios function [112]; a modified Gaussian function with symmetric power-law tails on both sides of the distributio”
Bayesian estimation supersedes the t test. (2013, background): “It turns out that in most typical applications, clumpiness can be adequately smoothed simply by running a long chain without thinning, and the long chain produces reliable estimates of the posterior distribution (e.g., Link & Eaton, 2012; Jackman, 2009, p. 263).”
On Using Bayesian Methods to Address Small Sample Problems (2016, background): “…help the accuracy of predictions; interval estimates have a more intuitive interpretation because the data are viewed as fixed rather than random (Jackman, 2009); hierarchical models can be easily extended to data with multilevel levels, missing data, or for multiple comparisons (Gelman, Hill &…”
General Relativity and Cosmology: Unsolved Questions and Future Directions (2016, background): “to be developed, for example). At best, we know which alternatives are ruled out by the data. This level of Bayesian inference is the application of a principle that has been called ‘Cromwell’s Rule’ [528]: even if all the data show our theory to be correct, we should allow a non-zero probability, e”
A Bayesian Approach for Estimating Multilevel Latent Contextual Models (2016, background): “Bayesian point and interval estimates can be derived when the chain has converged and approximates the posterior distribution sufficiently well (for an introduction, see Christensen, Johnson, Branscum, & Hanson, 2011; Jackman, 2009; Lynch, 2007).”
Journal of the American Statistical Association Bayesian Inference for Logistic Models Using Pólya–gamma Latent Variables Bayesian Inference for Logistic Models Using P ´ Olya–gamma Latent Variables (neutral): “Perhaps as a result, the Bayesian treatment of the logit model has not seen widespread adoption by nonstatisticians in the way that, for example, the Bayesian probit model is used extensively in both political science and market research (e.g., Rossi, Allenby, and McCulloch 2005; Jackman 2009).”
Spatial and spatio-temporal models with R-INLA. (2013, background): “During the last three decades, Bayesian methods have developed greatly and are now widely established in many research areas, from clinical trials (Berry et al., 2011), to health economic assessment (Baio, 2012) to the social sciences (Jackman, 2009), to epidemiology (Greenland, 2006).”
Bayesian Estimation and Inference (2015, neutral): “An exemplary book is by Gill (2008), but many others are also very good (e.g., Bolstad, 2007; Christensen, Johnson, & Branscum, 2011; Gelman et al., 2004; Hoff, 2009; Jackman, 2009; Kruschke, 2011; Lee, 2004; Lynch, 2010; for concise pro-Bayesian arguments, see Robert, 2007, chap.”
On a signal detection approach to m-alternative forced choice with bias, with maximum likelihood and Bayesian approaches to estimation (2012, methodology): “Some useful textbooks on Bayesian analysis are Congdon (2005), Gelman, Carlin, Stern, and Rubin (2004), Jackman (2009), and Lynch (2007).”