Statistics Seminars: Latent Branching Trees: Modelling and Bayesian Computation.
12 February 2018 14:00 in CM221
In this talk a novel class of semi-parametric time series models will be presented, 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'. It will be demonstrated how can we 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 this model and we will also discuss how this kind of models can be in other settings.
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