Specific themes of interest

Some specific themes we're interested in include:

  • How can we best use samples once they've been collected?
    • What estimators are more appropriate than the crude MC estimator for estimating expectations?
    • How can such estimators be constructed, analyzed, and applied?
  • How do we know when we've sampled enough?
    • What MCMC convergence diagnostics are available, and when can we trust them?
  • Which MCMC algorithm when?
    • An extremely wide variety of sampling algorithms have been developed, often targeting a specific type of pathology (e.g. highly skewed distributions, local correlation structures, etc.). How can we systematically diagnose which sampling algorithm is most appropriate for a given sampling problem?
  • Testing MCMC implementations
    • Since MCMC is often the only feasible way to compute a given quantity and its output is stochastic, how can we test that our implementations are correct?
  • Hybrid Monte Carlo and molecular dynamics
    • Which methods are most efficient for sampling conformational distributions of large solvated systems?
  • Nonequilibrium methods
    • How can we use nonequilibrium fluctuation theorems to analyze and correct time-discretized SDEs?