Theodore KYPRAIOS (University of Nottingham)

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.