Sequential Monte Carlo methods, also known as particle filters, have over the past two decades emerged as very successful tools for computational inference in statistical models, including (but not limited to) nonlinear dynamical systems. The workshop will bring together researchers in this area to discuss recent contributions, applications, and current challenges related to Sequential Monte Carlo.

In connection with the workshop there will also be an intensive PhD course on SMC on August 24-29.