"Robust inference for general projection structures by empirical Bayes and penalization methods"

"Posterior concentration for Bayesian regression trees and their ensembles"

"Discovering Interactions Using Covariate Informed Random Partition Models"

Non-Commutative Information Geometry   Information geometry is concerned with the study of statistical manifolds. These are differentiable manifolds consisting of probability distributions. In the param- eterized case their geometry is described by a metric tensor and a pair of dually flat connections. In the more general non-parameterized case they are Banach manifolds. This area of…

Estimating the mean response in a missing data model We study semiparametric Bayesian estimation of the mean response in a binary regression model with missing observations. We allow some dependence between the missingness and response mechanisms, which we assume are conditionally independent given some measured covariates (i.e. unconfoundedness). This model has applications in biostatistics and…

Latent Branching Trees: Modelling and Bayesian Computation. In this talk a novel class of semi-parametric time series models will bepresented, for which we can specify in advance the marginal distributionof the observations and then build the dependence structure of theobservations around them by introducing an underlying stochastic processtermed as 'latent branching tree'. It will be…

Modelling Preference Data with the Wallenius Distribution The Wallenius distribution is a generalisation of the Hypergeometric distribution where weights are assigned to balls of different colours. This naturally defines a model for ranking categories which can be used for classification purposes. Since, in general, the resulting likelihood is not analytically available, we adopt an approximate…

Compound Random Measures Compound Random Measures (CoRM's) have been recently introduced by Griffin and Leisen (2017) and represent a general and tractable class of vectors of Completely Random Measures.  This talk aims to provide an overview about CoRM's by illustrating some recent developments about their use in Bayesian nonparametrics.

Saddlepoint techniques for dependent data Saddlepoint techniques provide numerically accurate, higher-order, small sample approximations to the distribution of estimators and test statistics. While a rich theory is available for saddlepoint techniques in the case of independently and identically distributed observations, only a few results have been obtained for dependent data. In this talk, we explain…

Stochastic Differential Equations as Jets We explain how Ito Stochastic Differential Equations (SDEs) on manifolds may be defined using 2-jets of smooth functions. We show how this relationship can be interpreted in terms of a convergent numerical scheme. We show how jets can be used to derive graphical representations of Ito SDEs and how jets…

Wasserstein distances and other metrics for discretely observed Lévy processes We present some upper bounds for the Wasserstein distance of order p between the product measures associated with the increments of Lévy processes with possibly infinite Lévy measures. As an application, we derive an upper bound for the total variation distance between the marginals of…

Exact Bayesian inference for discretely observed jump-diffusions The standard approach to inference for parametric diffusion processesrelies on discretisation techniques (such as the Euler method) thatintroduce an approximation error difficult to quantify especially fordiscontinuous models, like jump-diffusions.In this talk, I will present methodology for exact inference thatavoids discretisation errors and allows to design MCMC samplerstargeting the…