Dr Matt Sutton, Imke Botha and Dr Joshua Bon
Queensland University of Technology

An Introduction to Sequential Monte Carlo

Sequential Monte Carlo (SMC) is a versatile algorithmic tool for data science, machine learning and statistics. It can be used for a myriad of inferential problems dealing with latent state prediction and parameter estimation. SMC Samplers provide robust parameter inference with uncertainty quantification for static Bayesian models. Whilst Particle filters, a subset of SMC algorithms, are popular for efficient estimation of latent states in hidden Markov models, beyond the limitations of the Kalman filter. This one-day introduction will orientate you to the world of SMC, demystify notation, and provide you with some hands on coding experience with SMC in Julia.


  1. An introduction to SMC Samplers (Matt Sutton)
  2. An introduction to Particle Filters (Imke Botha)
  3. An introduction to general SMC  (Joshua Bon)


This course assumes an undergraduate level understanding of statistics. Coding will use Julia, with a focus on understanding the template code and making adjustments.

Introduction to Sequential Monte Carlo (SMC)

Dr Matt Sutton
Queensland University of Technology

Dr Sutton is a postdoc researcher with a strong interest in developing new methods and algorithms for modern statistical challenges.  He is particularly interested in Bayesian statistics and computational challenges involving: working with big data, model averaging, and conducting inference with privacy constraints.

Imke Botha
Queensland University of Technology

Imke Botha is a PhD candidate at the Queensland University of Technology. Her research interests include sequential Monte Carlo, Markov chain Monte Carlo and pseudo-marginal methods. Her current research focuses on Bayesian inference for intractable likelihood models.

Dr Joshua Bon
Queensland University of Technology

Dr Joshua Bon is a Research Fellow in sequential Monte Carlo at the Queensland University of Technology. His research involves developing efficient and robust algorithms for Bayesian inference along with their mathematical justifications.