Tutorial-style seminar on Sequential Monte Carlo methods in Statistics

Anthony Lee, Senior Lecturer in Statistical Science at the University of Bristol will introduce Sequential Monte Carlo (SMC) methodology from a Statistics perspective. This particle-based algorithm was initially proposed to approximate predictive and filtering distributions for general state-space hidden Markov models, and in this context it is also known as a Particle Filter.


A special guest of the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Anthony's research is primarily in the area of stochastic algorithms for approximating intractable quantities that arise in data analysis. Examples of such algorithms are Markov chain and Sequential Monte Carlo.

His research in this area is interdisciplinary, bringing together advances in applied probability, algorithms, and statistics.

He will introduce Sequential Monte Carlo (SMC) methodology from a Statistics perspective, covering the basic algorithm and its properties, as well as some innovations that have improved its performance and extended its impact.

No previous knowledge of SMC is required for this seminar.

When: Tuesday 16 July, 11am
Where:  Room G07, Engineering Maths Building

About Anthony Lee

Anthony Lee is Senior Lecturer in Statistical Science in the School of Mathematics at the University of Bristol, and Director of the Data Science at Scale Programme at the Alan Turing Institute.

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