Spatial gamma processes in disease mapping In this talk we will present Bayesian models based on Markov random fields of gamma type to model the relative risk in disease mapping data. The spatial gamma processes allow for different spatial dependence among neighbours. We describe the properties of ths processes and use them as prior distributions…

Dependent species sampling models We consider a novel Bayesian nonparametric model for density estimation with an underlying spatial structure. The model is built on a class of species sampling models, which are discrete random probability measures that can be represented as a mixture of random support points and random weights. Specifically, we construct a collection…

Generalized partially additive Bayesian spectral analysis regression models In this talk, we present a Bayesian method for generalized partially additive regression using a spectral analysis of Gaussian process priors  for the  regression function. The smoothing prior distribution for the spectral coefficients incorporates hyper parameters that control the smoothness of the function and the tradeoff between…

False discovery rate smoothing Many approaches for multiple testing begin with the assumption that all tests in a given study should be combined into a global false-discovery-rate analysis.  But this may be inappropriate for many of today's large-scale screening problems, where test statistics have a natural spatial lattice structure (voxels in the brain, distance along…

SMC Samplers for Applications in High Dimensions Sequential Monte Carlo (SMC) methods are nowadays routinely applied in a variety of complex applications: hidden Markov models, dynamical systems, target tracking, control problems, just to name a few. Whereas SMC methods have been greatly refined in the last decades and are now much better understood, they are…

Convergence rates of posterior distributions in nonparametric inverse problems Since the seminal works of Ghosal, Ghosh and van der Vaart (2000) and Shen and Wasserman (2001), posterior contraction has attracted much attention, resulting in the rich literature on this subject. However, these results are not suitable to deal with trully ill-posed inverse problems, where one…

A Flexible Bayesian Approach to Monotone Missing Data in Longitudinal Studies with Informative Missingness with Application to An Acute Schizophrenia Clinical Trial We develop a Bayesian nonparametric model for a longitudinal response in the presence of nonignorable missing data. Our general approach is to first specify a {em working model} that flexibly models the missingness…

Behaviour of the posterior distribution in HMM models when the number of states is misspecified In this paper we study the asymptotic behaviour of the posterior distribution for parametric HMM models with finite number of components. We concentrate in particular on the case where the number of states of the hidden Markov chain in the…

On the ancestral process of long-range seed bank models It has been observed that, in some bacterial species, spores may remain dormant for a long time, to wake up much later, even up to "order of population size" generations later. When they wake up, they can still participate in the population's reproduction. This incredibly relaxed…

The scaling limit of the minimum spanning tree of the complete graph Consider the complete graph on n vertices with independent and identically distributed edge-weights having some absolutely continuous distribution. The minimum spanning tree (MST) is simply the spanning subtree of smallest weight.  It is straightforward to construct the MST using one of several natural…

Sequential Quasi Monte Carlo We develop a new class of algorithms, SQMC (Sequential Quasi Monte Carlo), as a variant of SMC (Sequential Monte Carlo) based on low-discrepancy points. The complexity of SQMC is O(N log N) where N is the number of simulations at each iteration, and its error rate is smaller than the Monte…

The algebraic method in experimental designs This seminar is part of the 3rd Carlo Alberto Stochastics Workshop

Chain event graphs and the geometry of causation This seminar is part of the 3rd Carlo Alberto Stochastics Workshop

Testability and consistency Bayesian consistency theorems come in (at least) three distinct types, e.g. Doob's prior-almost-sure consistency on Polish spaces, Schwartz's Hellinger consistency with KL-priors and the `tailfree' weak consistency of Dirichlet posteriors. In this talk we ask the question how these notions of convergence are related and argue that one characterises them most conveniently…

Adaptive testing of conditional association through recursive mixture modeling In many case-control studies, a central goal is to test for association or dependence between the predictors and the response. Relevant covariates must be conditioned on to avoid false positives and loss in power. Conditioning on covariates is easy in parametric frameworks such as the logistic…

Deviance Information Criteria for the frailty model We are concerned with model selection for the frailty model by use of the deviance information criterion (DIC). The DIC is a Bayesian model selection criterion proposed by Spiegelhalter et al. (2002).  A difficulty in applying the DIC to the frailty model lies on the unspecified baseline hazard…

A Bayesian nonparametric model for undirected and multi-edges networks In this talk, I will present ongoing work on a Bayesian nonparametric specification for either undirected or multi-edge directed networks, building on the framework of completely random measures. The formulation allows for an unbounded number of nodes in the network, while encouraging a sparse set of…

Bayesian inference in two–piece and skew–symmetric distributions using Jeffreys priors We study the Jeffreys prior and the independence Jeffreys prior of general classes of univariate location–scale two–piece and skew–symmetric models. For the case of two– piece models, Jeffreys priors are shown not to allow for Bayesian inference in the wide and practically relevant class of…

Bayesian density estimation for compositional data using random Bernstein polynomials We propose a Bayesian nonparametric model for single density estimation, for data in the p-dimensional simplex space, say S_p. The proposal is based on a particular class of multivariate Bernstein polynomials on S_p and extends the Dirichlet-Bernstein prior for density estimation, for data in a closed,…