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BUGS is used for multi-level modeling: using a specialized notation, you can define random variables of various distributions, set Bayesian priors for their parameters...

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2004. Bayesian multilevel estimation with poststratification: State-level estimates from national polls. Shor, Boris. 2006. A Bayesian multilevel model of federal spending, 1983-2001.

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Seltzer, M. (1994). Studying variation in program success: A multilevel modeling approach. Evaluation Review, 18, 342-361. Earlier work on the use of MCMC in Bayesian Analysis of Multilevel Data: Seltzer, M. & Choi, K. (2002). Model checking and sensitivity analysis for multilevel models.

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Jun 19, 2016 · Bayesian modeling has two major advantages over frequentist analysis with linear mixed models. First, information based on preceding results can be incoportated using dierent priors. Second, complex models with a large number of random variance components can be ﬁt. In the following, we will provide a short introduction to Bayesian statistics.

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In principle, the Bayesian approach to model selection is straightforward. Prior probability distributions are used to describe the uncertainty surround-ing all unknowns. After observing the data, the posterior...

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The Bayesian paradigm is particularly useful for the type of data that social scientists encounter given its recognition of the mobility of pop- ulation parameters, its ability to incorporate information from prior research, and its ability to update estimates as new data are observed.