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