Sequential Monte Carlo (SMC) is a robust computational method for simulation-based inference including Bayesian statistics. It can facilitate sampling from complex probability distributions and optimising challenging functions where other methods fail. Exciting new research has extended SMC to distributed settings and developed novel SMC algorithms for new applications. This workshop will foster discussion and collaboration around new and important SMC and particle methods, including new applications of SMC to real world problems. To spur discussion and interest, the workshop content will be multifaceted. It will include an introduction to SMC, opportunities for attendees to highlight and disseminate cutting-edge research, talks on best-practice use of SMC and new SMC algorithms, as well as theoretical developments in SMC. During the workshop, ample time will be devoted to facilitated small-group discussions on new opportunities in SMC applications and novel algorithmic and theoretical advancements.
The organisation of this workshop has considered a wide range of people, making reasonable adjustments to its location and format to ensure that barriers are removed that would otherwise prevent participation.


  • Mr Adam Bretherton, QUT
  • Dr Joshua Bon, QUT
  • Professor Chris Drovandi, QUT
  • Ms Sarah Vollert, QUT
  • Professor Kerrie Mengersen, QUT


This workshop is supported by AMSI and AustMS through the AMSI-AustMS Workshop Funding program.

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