In this section, we consider geometric fragmentations; that is, we assume that the set of r∈(0,1) such that

We study a Markovian model for the random fragmentation of an object. At each time, the state consists of a collection of blocks. Each block waits an…

The Wisconsin Longitudinal Study (wls) is a panel study of over 10,000 people who graduated from Wisconsin High Schools in 1957. We consider males who, when asked in 1975, had either been drafted or had not served in the military at all; after remov…

We provide a parameterization of the discrete nested Markov model, which is a supermodel that approximates DAG models (Bayesian network models) with …

In this paper we consider two continuous-mass population models as analogues of logistic branching random walks, one is supported on a finite trait s…

Bayesian nonparametric approaches, in particular the Pitman-Yor process and the associated two-parameter Chinese Restaurant process, have been succes…

We present a novel Bayesian nonparametric regression model for covariates X and continuous, real response variable Y. The model is parametrized in te…

While a typical supervised learning framework assumes that the inputs and the outputs are measured at the same levels of granularity, many applicatio…

Markov chain Monte Carlo (MCMC) algorithms are ubiquitous in Bayesian computations. However, they need to access the full data set in order to evalua…

The reparameterization trick enables optimizing large scale stochastic computation graphs via gradient descent. The essence of the trick is to refact…

Given a measurement graph $G= (V,E)$ and an unknown signal $r \in \mathbb{R}^n$, we investigate algorithms for recovering $r$ from pairwise measureme…

While variational dropout approaches have been shown to be effective for network sparsification, they are still suboptimal in the sense that they set…

We construct a stationary Markov process corresponding to the evolution of masses and distances of subtrees along the spine from the root to a branch…

Nonparametric and nonlinear measures of statistical dependence between pairs of random variables are important tools in modern data analysis. In part…

Boylan [9] established that the family of local times of (1+α)-stable Lévy processes admits a version that is Hölder continuous of order ν∈(0,α/2) in the spatial direction, uniformly in space and time on any compact space-time rectangles. Barlow [3]…

We establish two results about local times of spectrally positive stable processes. The first is a general approximation result, uniform in space and…

A clique colouring of a graph is a colouring of the vertices so that no maximal clique is monochromatic (ignoring isolated vertices). The smallest nu…

We have studied numerically the random interchange model and related loop models on the three-dimensional cubic lattice. We have determined the trans…

In the Bayesian approach, the a priori knowledge about the input of a mathematical model is described via a probability measure. The joint distributi…

We present an illustration on synthetic data of the estimators introduced in Section 2. We also consider other estimators of τ1 that have been proposed in the literature of disclosure risk assessment: i) two parametric empirical Bayes estimators of …

Protection against disclosure is a legal and ethical obligation for agencies releasing microdata files for public use. Consider a microdata sample of…

Let 0<p=p(n)<1 be such that p is bounded away from 1 and p>n−1/3+ε, for some positive ε<1/3. Suppose t=t(n)≥0 and δ=δ(n)>0 satisfy t=o(lnn/lnlnn) and t2lnlnn/lnn=o(pδ). Let ^αt,p(n) be as defined in (1). If k=k(n)=⌊^αt,p(n)−δ⌋, then

For the Erd\H{o}s-R\'enyi random graph G(n,p), we give a precise asymptotic formula for the size of a largest vertex subset in G(n,p) that induces a …

We highlight some of the features of our contribution and outline directions for future research.

We analyse the learning performance of Distributed Gradient Descent in the context of multi-agent decentralised non-parametric regression with the sq…

Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instan…

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