Introduction to Bayesian Statistics

By Samiran Sinha. 2008

810 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2bd629ea9559289bc19d6e5691d2ab4c1dee6b4f

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Citations in the corpus

Improving Transparency and Replication in Bayesian Statistics: The WAMBS-Checklist (2017, neutral): “For a full introduction to Bayesian modeling, we refer the 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”
kml and kml3d: R Packages to Cluster Longitudinal Data (2015, methodology): “Considering extra hypothesis, some parametric criteria like BIC (Schwarz 1978), AIC (Akaike 1974), AIC with correction for ﬁnite sample size (Hurvich and Tsai 1989) or the global posterior probability (Bolstad 2007) can also be computed.”
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.”
The seven sins of L2 research: A review of 30 journals’ statistical quality and their CiteScore, SJR, SNIP, JCR Impact Factors (2019, background): “Some statisticians have even gone as far as to describe confidence intervals as ‘scientifically useless’ (Bolstad, 2007, p. 228).”
On the Bayesian analysis of the mixture of power function distribution using the complete and the censored sample (2010, methodology): “Bolstad [4] and Bansal [3] have given a detailed discussion of the predictive distribution.”
On the formulation of the alternative hypothesis for geodetic outlier detection (2013, background): “This choice is justified by the principle of maximum entropy (Koch 2007).”
What Does It Mean to be Biased: Motivated Reasoning and Rationality (2014, methodology): “In statistics (and related disciplines such as machine learning) the term “bias” refers to (expected) systematic deviation (see e.g., Bolstad, 2004).”
For and Against Methodologies: Some Perspectives on Recent Causal and Statistical Inference Debates (2017, background): “…argued, causal models and diagrams are incredibly useful for avoiding bad judgments, causal fallacies, harmful adjustments, and for identifying sources of bias and valid adjustments (Pearl 1995, 2009; Greenland et al. 1999; Höfler 2005; Phillips and Goodman 2004, 2006; Glymour and Greenland 2008).”
BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks (2020, neutral): “If the covariance matrix Σ is known, the posterior of the mean vector μ has a closed-form which is also a MVN p(μ |x,Σ) = Nd(μ |m,Λ) with posterior precision matrix given by Λ−1 = I + Σ−1 and posterior mean given by m = ΛΣ−1x [6].”