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Latent Branching Trees

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If you have a question about this talk, please contact Zoubin Ghahramani.

This talk is concerned with a class of semi-parametric time series models for which we can specify in advance the marginal distribution of the observations and then build the dependence structure of the observations around them by introducing an underlying stochastic process termed as ‘latent branching tree’. We will demonstrate how one can draw Bayesian inference for the model parameters using Markov Chain Monte Carlo methods as well as Approximate Bayesian Computation methodology. Finally a real dataset on genome scheme data will be fitted to these models and we will also discuss how this kind of models can be used in modelling Internet traffic.

This talk is part of the Machine Learning @ CUED series.

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