6th Workshop on Sequential Monte Carlo Methods (SMC 2024)

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6th Workshop on Sequential Monte Carlo Methods (SMC 2024)

 13 - 17 May 2024

ICMS, Bayes Centre, Edinburgh


Recordings from this workshop are being processed. They will appear on this webpage and our YouTube channel shortly.

Scientific Organisers:

  • Víctor Elvira, University of Edinburgh
  • Dan Crisan, Imperial College London
  • Jana de Wiljes, University of Potsdam
  • Joaquín Míguez, University Carlos III of Madrid


Sequential Monte Carlo (SMC) methods, also known as particle filters or particle methods, have become popular and powerful tools for computational inference in complex probabilistic models used in many and varied fields and applications. The research community includes practitioners and theoreticians at the intersection of statistics, computer science, electrical engineering, and applied mathematics. The interest in SMC methods have rapidly grown in the last decade, jointly with the new challenges and the enormous potential of SMC to tackle high-impact problems in applied sciences (e.g., meteorology, biomedicine, robotics, etc.). The main objectives of the meeting are:  

1. to bring together researchers developing and using SMC methods in a diversity of scientific and engineering fields, both in academia and the industry, and

2. to open the field to researchers and users who are new to SMC methods. We will emphasize the interaction of SMC with related areas of research (such as machine learning or data science) where computational inference plays a key role.

Below, we provide a list of key theoretical and methodological topics, as well as application areas.

  Theory & Methodology

  • Particle filtering/SMC methods

  • Kalman filtering

  • Data assimilation

  • State space models

  • Numerical methods for stochastic partial differential equations (SPDEs)

  • Adaptive importance sampling

  • Sequential Markov-chain Monte Carlo (MCMC) methods

  • Stability of optimal filters

  • Multi-level Monte Carlo


  • Multi-target tracking

  • Radar and sonar signal processing

  • Localization and tracking with sensor networks

  • Numerical weather prediction

  • Climate modeling and prediction

  • Biomedical signal processing

  • Astrophysics and cosmology

  • Smart grids and industrial applications

  • Quantitative finance

Finally, this event is linked to the Summer School on Bayesian filtering: fundamental theory and numerical methods (SSBF), which will be held also at ICMS on 6-10 May 2024 (i.e., the week before this workshop).


MONDAY 13 MAY 2024
Registration and Refreshments
Welcome and Housekeeping
Christian Robert, Université Paris Dauphine PSL & University of Warwick Sampling advances by adaptive regenerative processes and importance Monte Carlo
Sahani Pathiraja, UNSW Sydney Connections between sequential filtering and evolutionary dynamics
Joaquín Míguez, Universidad Carlos III de Madrid A Sequential Discretisation Scheme for Stochastic Differential Equations and Its Application to Bayesian Filtering
Axel Finke, Loughborough University Particle­-MALA and Particle­-mGRAD: Gradient­-based MCMC methods for high­-dimensional state-space models
Francesca Crucinio, King's College London A connection between Tempering and Entropic Mirror Descent
Fredrik Lindsten, Linköping University Sequential Monte Carlo guidance of (discrete) diffusion models
Poster session 1 Not available online
Christophe Andrieu, University of Bristol Monte Carlo sampling with integrator snippets
Anthony Lee, University of Bristol Mixing time of the conditional backward sampling particle filter
Arnaud Doucet, University of Oxford & Google DeepMind Diffusion models for Monte Carlo sampling
Meetings & discussion
Yunpeng Li, University of Surrey Normalising flow-based differentiable particle filters
Neil Chada, Heriot Watt University Multilevel Bayesian Deep Neural Networks
Poster session 2 Not available online
Conference Dinner hosted at South Hall (Pollock Estate)
Nick Whiteley, University of Bristol Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods
Jana de Wiljes, TU Ilmenau
Oana Lang, Imperial College London Generative Modelling for a Stochastic Rotating Shallow Water System
Meetings & discussion
Nicola Branchini, University of Edinburgh Generalizing self-normalized importance sampling with couplings
Simo Särkkä, Aalto University Parallel filtering and smoothing methods for state-space models
Poster session 3 Not available online
Pierre Del Moral, Inria Some theoretical aspects of Particle Filters and Ensemble Kalman Filters
Alexandros Beskos, University College London Antithetic Multilevel Methods for Elliptic and Hypo-Elliptic Diffusions
Daniel Paulin, University of Edinburgh Unbiased Kinetic Langevin Monte Carlo with Inexact Gradients
Adam Johansen, University of Warwick Divide and Conquer Sequential Monte Carlo: Some Properties and Application
Meetings & discussion
FRIDAY 17 MAY 2024
Sara Pérez Vieites, Aalto University Learning the number of particles in nested filtering
Marcelo Gomes da Silva Bruno, ITA, Brazil Sequential Monte Carlo Methods for Distributed Bayesian Filtering on Manifolds
Jeremy Heng, ESSEC Business School Computational Doob's h-transforms for online filtering
Nicolas Chopin, ENSAE Paris, IPP Unbiased estimation of smooth functions
Lunch and end of workshop

Sponsors and Funders:

  • ICMS
  • EMS