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Sequential Monte-Carlo Methods

Sequential Monte-Carlo Methods

This video was recorded at 23rd Annual Conference on Neural Information Processing Systems (NIPS), Vancouver 2009. Over the last fifteen years, sequential Monte Carlo (SMC) methods gained popularity as powerful tools for solving intractable inference problems arising in the modelling of sequential data. Much effort was devoted to the development of SMC methods, known as particle filters (PFs), for estimating the filtering distribution of the latent variables in dynamic models. This line of research produced many algorithms, including auxiliary-variable PFs, marginal PFs, the resample-move algorithm and Rao-Blackwellised PFs. It also led to many applications in tracking, computer vision, robotics and econometrics. The theoretical properties of these methods were also studied extensively in... Show More
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