Not inference for graphical models, but graphical inference.

Buja, A et al. (2009). Statistical inference for exploratory data analysis and model diagnostics.

*Philosophical Transactions of the Royal Society A*,*367*(1906), 4361–4383. doi:10.1098/rsta.2009.0120Wickham, Hadley, Cook, Dianne, Hofmann, Heike, & Buja, Andreas (2010). Graphical inference for infovis.

*IEEE Transactions on Visualization and Computer Graphics*,*16*(6), 973–979. doi:10.1109/TVCG.2010.161A general method of graphical inference: make a plot that should reveal whatever phenomenon you’re interested, and then hide it amidst many other plots generated from the null. Led to the nullabor R package.

Loy, A., Follett, L., & Hofmann, Heike (2016). Variations of Q-Q Plots: The Power of Our Eyes!

*The American Statistician*,*70*(2), 202–214. doi:10.1080/00031305.2015.1077728Uses graphical inference to test for normality using Q-Q plots, and even compares power to classical tests by using a Mechanical Turk study. Finds that graphical tests have several times the power, but comparable error rates.

Wild, C. J., Pfannkuch, M., Regan, M., & Horton, N. J. (2011). Towards more accessible conceptions of statistical inference [with discussion].

*Journal of the Royal Statistical Society Series A (Statistics in Society)*,*174*(2), 247–295. doi:10.1111/j.1467-985X.2010.00678.xDiscusses introducing the core concepts of statistical inference using entirely visual methods. (See Teaching statistics for more discussion.)