Professor Robin Evans
My research is in understanding how multivariate statistical models - such as Bayesian network models, marginal models and latent variable models - can be used to learn about the world around us. In addition, I am interested in how these methods can be applied to epidemiology, medicine and the social sciences.
This work has led in various directions: finding implications of models that can be tested in data, and showing that no further constraints exist; determining whether quantities of scientific interest are identifiable; understanding when marginal models can be properly specified, simulated from, and fitted; and understanding when efficient model selection is possible.
I am also interested in the mathematical and statistical properties of more general latent variable models, conditional independence, and model parametrizations.
I received my PhD in Statistics from the University of Washington in 2011, and was a Postdoctoral Research Fellow at the Statistical Laboratory in Cambridge from 2011 to 2013.
R.J. Evans, Graphs for margins of Bayesian networks. Scandinavian Journal of Statistics, 43 (3), pp. 625-648, 2016.
R. Silva and R.J. Evans, Causal Inference through a Witness Protection Program. Journal of Machine Learning Research, 17 (56), pp. 1-53, 2016.
R.J. Evans and T.S. Richardson, Markovian acyclic directed mixed graphs for discrete data. Annals of Statistics, 42 (4), pp. 1452-1482, 2014.
R.J. Evans and T.S. Richardson, Marginal log-linear parameters for graphical Markov models. Journal
of the Royal Statistical Society, Series B, 75 (4), pp. 743-768, 2013.