Undirected graphical model has proven to be a useful abstraction in statistics and machine learning. The starting point is the graph of a distribution. While often the graph is assumed given, we are interested in estimating the graph from data. In this talk we present new nonparametric and semiparametric methods for graph estimation. The performance…
To carry out posterior inference on datasets of unprecedented sizes, practitioners are turning to biased MCMC procedures that trade off asymptotic exactness for computational efficiency. The reasoning is sound: a reduction in variance due to more rapid sampling can outweigh the bias introduced. However, the inexactness creates new challenges for sampler and parameter selection, since…
Undirected graphical model has proven to be a useful abstraction in statistics and machine learning. The starting point is the graph of a distribution. While often the graph is assumed given, we are interested in estimating the graph from data. In this talk we present new nonparametric and semiparametric methods for graph estimation. The performance…
To carry out posterior inference on datasets of unprecedented sizes, practitioners are turning to biased MCMC procedures that trade off asymptotic exactness for computational efficiency. The reasoning is sound: a reduction in variance due to more rapid sampling can outweigh the bias introduced. However, the inexactness creates new challenges for sampler and parameter selection, since…
