Sequential Monte Carlo: Introduction and Diagnostics by Pedro German Ramirez & Osvaldo Martin

Talk Abstract

In this talk we will provide a brief introduction to Sequential Monte Carlo (SMC) methods and provide a guide to diagnose posterior samples computed using SMC.

Pedro German Ramirez GitHub pgerramirez
Osvaldo Martin Twitter @aloctavodia GitHub aloctavodia


Pedro German Ramirez

In the year 2014 I completed my Bs. in Molecular Biology at the National University of San Luis, Argentina and in 2020 I finished my PhD in the Instute of Applied Mathematics (IMASL) while working within the Structural Bioinformatics Group (BIOS). My PhD thesis was centered around the use of a statiscal mechanics model to simulate biologically relevant systems of peptide-lipid interactions. Currently I’m doing my postdoc alongside Dr. Osvaldo Martin on probabilistic modeling and Sequential Monte Carlo.

Osvaldo Martin

Osvaldo is a researcher at the National Scientific and Technical Research Council in Argentina and is notably the author of the book Bayesian Analysis with Python, whose second edition was published in December 2018. He also teaches bioinformatics, data science and Bayesian data analysis, and is a core developer of PyMC3 and ArviZ, and recently started contributing to Bambi. Originally a biologist and physicist, Osvaldo trained himself to python and Bayesian methods – and what he’s doing with it is pretty amazing!

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Thanks for the talk! Very clear introduction of SMC - just want to say that I am interest to compare the performance of Independent MH and HMC as well.


We should definitely do that!


Excellent talk – thank you! I will have to explore the connection between SMC and ABC. I was thinking about it when you showed the diagram, and then you mentioned it on the next slide. Interesting stuff.