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• The No-U-Turn Sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. (Matt Hoffman and Andrew Gelman)

• Inherent difficulties of non-Bayesian likelihood-based inference, as revealed by an examination of a recent book by Aitkin. (Andrew Gelman, Christian Robert, and Judith Rousseau)

• Analytical and graphical methods for understanding hierarchical models with an application to estimating state trends in death penalty public opinion. (Kenny Shirley and Andrew Gelman)

• Visualizing distributions of covariance matrices. (Tomoki Tokuda, Ben Goodrich, Iven Van Mechelen, Andrew Gelman, and Francis Tuerlinckx)

• Avoiding boundary estimates in linear mixed models through weakly informative priors. (Yeojin Chung, Sophia Rabe-Hesketh, Andrew Gelman, Jingchen Liu, and Vincent Dorie)

• On the stationary distribution of iterative imputations. (Jingchen Liu, Andrew Gelman, Jennifer Hill, and Yu-Sung Su)

• Infovis and statistical graphics: different goals, different looks. (Andrew Gelman and Antony Unwin)

• Deep interactions with MRP: presidential turnout and voting patterns among small electoral subgroups. (Yair Ghitza and Andrew Gelman)

• ``Not only defended but also applied'': The perceived absurdity of Bayesian inference. (Andrew Gelman and Christian Robert)

• ``How many zombies do you know?'': Using indirect survey methods to measure alien attacks and outbreaks of the undead. (Andrew Gelman)

• Thoughts on new statistical procedures for age-period-cohort analyses. (Andrew Gelman)

• Protecting minorities in binary elections: A test of storable votes using field data. (Alessandra Casella, Shuky Ehrenberg, Andrew Gelman, and Jie Shen)

• One vote, many Mexicos: income and vote choice in the 1994, 2000, and 2006 presidential elections. (Jeronimo Cortina, Andrew Gelman, and Narayani Lasala)

• Fitting multilevel models when predictors and group effects correlate. (Joseph Bafumi and Andrew Gelman)

• Fully Bayesian computing. (Jouni Kerman and Andrew Gelman)

• Understanding persuasion and activation in presidential campaigns: The random walk and mean-reversion models (Noah Kaplan, David Park, and Andrew Gelman)

• Sampling for Bayesian computation with large datasets. (Zaiying Huang and Andrew Gelman)

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