Given the abundance of data and the complexity of statistical models being used by statisticians and more general scientists today, likelihood-based statistical inference can be extremely challenging. This workshop will focus on modern technologies designed to address these challenges. As well as being sponsored through CRiSM, the workshop is supported by i-like.
Mark Girolami, Playing Russian Roulette with Intractable Likelihoods
Eric Moulines,The Island Particle model
Nicolas Chopin, Properties of the particle Gibbs sampler (Joint work with Sumeetpal S. Singh)
Cristiano Varin, "The Ranking Lasso"
Matti Vihola, Convergence Properties of Pseudo-Marginal Markov Chain Monte Carlo Algorithms
Iain Murray, Flexible density estimation applied to intractable priors
Jim Berger, Adaptive Importance Sampling and Exoplanet Discovery
Henry Wynn, Monomial ideals in probability and statistics
Matthew Stephens, Adaptive shrinkage, False Discovery Rates, and multiple comparisons:a generic approach via Laplace approximation
Richard Samworth, High-dimensional variable selection
Christian Robert, "ABC as the new empirical Bayes approach?"
Registration *NOW CLOSED*