Differentiable surrogate for simulation-based inference

Job description: Many models of complex phenomena (physics, molecular dynamics, etc.) have no global analytical expressions but admit implementations in silico in form of forward simulators. In turn, forward simulations are used to solve inverse problems: given observations of the phenomena find its initial conditions viewed as input parameters of the simulator. In statistical terms solving such an inverse problem corresponds to sampling from a (bayesian) posterior distribution with the implicit likelihood given by the simulator - this provides (at least) an answer to the problem with error bounds in form of uncertainty estimates. High dimensionality and/or computational load hinder use of simple classical methods (like ABC or Kernel Density Estimator) and lead to construction of surrogates that approximate the intractable likelihood coupled with amenable schemes for posterior sampling. Recent advances in Automatic Differentiation models allow construction of such surrogates which are yet in their early development. In this thesis we aim to study and develop new ways to construct differentiable surrogates and apply them on a number of realistic problems starting with a number of applications in nuclear imaging.

Your profile: Master 2 Probabilités / Statistiques / Apprentissage automatique