Bayesian Methods : A Social and Behavioral Sciences Approach

By Jeff Gill. 2002

866 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6740da7ff18ff54a35322dbd1a322bda0fafc403

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Citations in the corpus

Back analysis of slope failure with Markov chain Monte Carlo simulation (2010, neutral): “However, there is no systematic and universal way to calculate the number of samples that should be discarded [11].”
Multilevel Modelling of Country Effects: A Cautionary Tale (2016, background): “…in general multilevel modelling textbooks (for example, Raudenbush and Bryk, 2002: chapter 13; Brown and Prescott, 2006: section 2.3), review articles such as Draper (2008), and a number of textbooks focusing on Bayesian multilevel modelling such as Gelman et al. (2013) or Gill (2008).”
Using Copulas to Model Time Dependence in Stochastic Frontier Models (2014, methodology): “One possibility would be generic Markov Chain Monte Carlo methods such as the Metropolis–Hastings algorithm (see, e.g., Robert and Casella, 2004; Gill, 2008).”
Bayesian Mediation Analysis (2009, neutral): “For an accessible and excellent introduction to Bayesian statistics, see texts by Gelman, Carlin, Stern, and Rubin (2003) and Gill (2008).”
Gibbs sampling, exponential families and orthogonal polynomials (2008, methodology): “M E ] 2 8 A ug 2 00 8 Key words and phrases: Gibbs sampler, running time analyses, exponential families, conjugate priors, location families, orthogonal polynomials, singular value decomposition.”
Predicting human behavior in unrepeated, simultaneous-move games (2013, methodology): “We combine experimental data with explicitly quantified prior beliefs to derive a posterior distribution that assigns probability to parameter settings in proportion to their consistency with the data and the prior (Gill, 2002).”
The BUGS project: Evolution, critique and future directions (2009, methodology): “The software is also used widely for the teaching of Bayesian modelling ideas to students and researchers the world over, and several texts use BUGS extensively for illustrating the Bayesian approach [19–26].”
The Markov chain Monte Carlo revolution (2008, background): “For political science-flavored applications, see [43].”
The Psychological Well-Being of Early Identified Gifted Children (2016, background): “…the case in this study) without the loss of power (due to, e.g., Bonferroni-type corrections, as elaborated in Hoijtink, Klugkist, & Boelen, 2008; Van de Schoot et al., 2011), and is especially suitable for small sample sizes, as it is not based on normality or asymptotic assumptions (Gill, 2008).”
Introduction to Applied Bayesian Statistics and Estimation for Social Scientists (2007, background): “To be sure, a variety of introductory books are available that cover the basics of the Bayesian approach to statistics (e.g., Gill 2002 and Gelman et al. 1995) and several that cover the foundation of MCMC methods (e.g., beginning with Gilks et al. 1996).”