## About

38

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Introduction

Bernardo Nipoti currently works at the Department of Economics, Management and Statistics of the University of Milano Bicocca, Italy. Bernardo does research in Bayesian Statistics, with focus on nonparametric methods.

**Skills and Expertise**

## Publications

Publications (38)

While the use of expected goals (xG) as a metric for assessing soccer performance is increasingly prevalent, the uncertainty associated with their estimates is often overlooked. This work bridges this gap by providing easy-to-implement methods for uncertainty quantification in xG estimates derived from Bayesian models. Based on a convenient posteri...

Several approaches have been proposed in the literature for clustering multivariate ordinal data. These methods typically treat missing values as absent information, rather than recognizing them as valuable for profiling population characteristics. To address this gap, we introduce a Bayesian nonparametric model for co-clustering multivariate ordin...

A dynamic treatment regime is a sequence of medical decisions that adapts to the evolving clinical status of a patient over time. To facilitate personalized care, it is crucial to assess the probability of each available treatment option being optimal for a specific patient, while also identifying the key prognostic factors that determine the optim...

The increasing availability of multiple network data has highlighted the need for statistical models for heterogeneous populations of networks. A convenient framework makes use of metrics to measure similarity between networks. In this context, we propose a novel Bayesian nonparametric model that identifies clusters of networks characterized by sim...

Motivated by an increasing demand for models that can effectively describe features of complex multivariate time series, e.g. from sensor data in biomechanics, motion analysis, and sports science, we introduce a novel state-space modeling framework where the state equation encodes the evolution of latent partitions of the data over time. Building o...

Background
Whilst Type 2 Diabetes Mellitus (T2DM) is an established risk factor for cognitive impairment, the underlying mechanisms remain poorly explored. One potential mechanism may be through effects of T2DM on cerebral perfusion. The current study hypothesised that T2DM is associated with altered peripheral and central haemodynamic responses to...

Background
Diabetes is associated with slower gait speed and adverse brain health outcomes in older adults. However, the putative mechanisms underlying these associations remain poorly explored. One such mechanism is via altered cerebral perfusion, which may represent an important intermediate phenotype in the association between diabetes and slowe...

Taking into account axial symmetry in the covariance function of a Gaussian random field is essential when the purpose is modelling data defined over a large portion of the sphere representing our planet. Axially symmetric covariance functions admit a convoluted spectral representation that makes modelling and inference difficult. This motivates th...

Nonparametric mixture models based on the Pitman–Yor process represent a flexible tool for density estimation and clustering. Natural generalization of the popular class of Dirichlet process mixture models, they allow for more robust inference on the number of components characterizing the distribution of the data. We propose a new sampling strateg...

Discrete Bayesian nonparametric models whose expectation is a convex linear combination of a point mass at some point of the support and a diffuse probability distribution allow to incorporate strong prior information, while still being extremely flexible. Recent contributions in the statistical literature have successfully implemented such a model...

BNPmix is an R package for Bayesian nonparametric multivariate density estimation, clustering, and regression, using Pitman-Yor mixture models, a flexible and robust generalization of the popular class of Dirichlet process mixture models. A variety of model specifications and state-of-the-art posterior samplers are implemented. In order to achieve...

Discrete Bayesian nonparametric models whose expectation is a convex linear combination of a point mass at some point of the support and a diffuse probability distribution allow to incorporate strong prior information, while still being extremely flexible. Recent contributions in the statistical literature have successfully implemented such a model...

The stratified proportional hazards model represents a simple solution to take into account heterogeneity within the data while keeping the multiplicative effect of the predictors on the hazard function. Strata are typically defined a priori by resorting to the values of a categorical covariate. A general framework is proposed, which allows the str...

The stratified proportional hazards model represents a simple solution to account for heterogeneity within the data while keeping the multiplicative effect on the hazard function. Strata are typically defined a priori by resorting to the values taken by a categorical covariate. A general framework is proposed, which allows for the stratification of...

Location-scale Dirichlet process mixtures of Gaussians (DPM-G) have proved extremely useful in dealing with density estimation and clustering problems in a wide range of domains. Motivated by an astronomical application, in this work we address the robustness of DPM-G models to affine transformations of the data, a natural requirement for any sensi...

Abstract We prove a monotonicity property of the Hurwitz zeta function which, in turn, translates into a chain of inequalities for polygamma functions of different orders. We provide a probabilistic interpretation of our result by exploiting a connection between Hurwitz zeta function and the cumulants of the beta-exponential distribution.

We prove a monotonicity property of the Hurwitz zeta function which, in turn, translates into a chain of inequalities for polygamma functions of different orders. We provide a probabilistic interpretation of our result by exploiting a connection between Hurwitz zeta function and the cumulants of the exponential-beta distribution.

Nonparametric mixture models based on the Pitman-Yor process represent a flexible tool for density estimation and clustering. Natural generalization of the popular class of Dirichlet process mixture models, they allow for more robust inference on the number of components characterizing the distribution of the data. We propose a new sampling strateg...

Location-scale Dirichlet process mixtures of Gaussians (DPM-G) have proved extremely useful in dealing with density estimation and clustering problems in a wide range of domains. Motivated by an astronomical application, in this work we address the robustness of DPM-G models to affine transformations of the data, a natural requirement for any sensi...

A standard approach for dealing with unobserved heterogeneity and clustered time‐to‐event data within the proportional hazards (PH) context has been the introduction of a cluster‐specific random effect (frailty), common to subjects within the same cluster. However, the conditional PH assumption could be too strong for some applications. For example...

For the most popular discrete nonparametric models, beyond the Dirichlet process, the prior guess at the shape of the data-generating distribution, also known as the base measure, is assumed to be diffuse. Such a specification greatly simplifies the derivation of analytical results, allowing for a straightforward implementation of Bayesian nonparam...

Given a sample of size n from a population of individuals belonging to different species with unknown proportions, a problem of practical interest consists in making inference on the probability Dn(l) that the (n + 1)-th draw coincides with a species with frequency l in the sample, for any l = 0, 1,.., n. This paper contributes to the methodology o...

In this paper, we consider homogeneous normalized random measures with independent increments (hNRMI), a class of nonparametric priors recently introduced in the literature. Many of their distributional properties are known by now but their stick-breaking representation is missing. Here we display such a representation, which will feature dependent...

Given a sample of size $n$ from a population of individual belonging to
different species with unknown proportions, a popular problem of practical
interest consists in making inference on the probability $D_{n}(l)$ that the
$(n+1)$-th draw coincides with a species with frequency $l$ in the sample, for
any $l=0,1,\ldots,n$. This paper contributes to...

Exact sampling methods have been recently developed for generating random variates for exponentially tilted α-stable distributions. In this paper we show how to generate, exactly, random variates for a more general class of tilted α-stable distributions, which is referred to as the class of Laguerre-type exponentially tilted α-stable distributions....

Species sampling problems have a long history in ecological and biological studies and a number of statistical issues, including the evaluation of species richness, are still to be addressed. In this paper, motivated by Bayesian nonparametric inference for species sampling problems, we consider the practically important and technically challenging...

We contribute to the discussion of the paper by Devroye and James, by reviewing some of the most meaningful results that relate the unilateral stable distribution with the asymptotic behavior of the so-called Ewens-Pitman sampling model. Our focus is then on how these results have been exploited in the context of Bayesian nonparametric inference fo...

The proposal and study of dependent prior processes has been a major research
focus in the recent Bayesian nonparametric literature. In this paper, we
introduce a flexible class of dependent nonparametric priors, investigate their
properties and derive a suitable sampling scheme which allows their concrete
implementation. The proposed class is obta...

Mixture models for hazard rate functions are widely used tools for addressing the statistical analysis of survival data subject to a censoring mechanism. The present article introduced a new class of vectors of random hazard rate functions that are expressed as kernel mixtures of dependent completely random measures. This leads to define dependent...

Bayesian nonparametric marginal methods typically yield point estimates in
the form of posterior expectations. Though very useful and easy to implement in
a variety of statistical inferential problems, these methods may suffer from
some limitations if used to estimate non--linear functionals of posterior
distributions, such as credible intervals. T...

Most of the Bayesian nonparametric models for non-exchangeable data that are used in applications are based on some extension to the multivariate setting of the Dirichlet process, the best known being MacEachern’s dependent Dirichlet process. A comparison of two recently introduced classes of vectors of dependent nonparametric priors, based on the...

The problem of estimating discovery probabilities originated in the context of statistical ecology, and in recent years it has become popular due to its frequent appearance in challenging applications arising in genetics, bioinformatics, linguistics, designs of experiments, machine learning, etc. A full range of statistical approaches, parametric a...

In this paper we investigate the stick-breaking representation for the class of σ-stable Poisson-Kingman models, also known as Gibbs-type random probability measures. This class includes as special cases most of the discrete priors commonly used in Bayesian nonparametrics, such as the two parameter Poisson-Dirichlet process and the normalized gener...

Bayesian nonparametric marginal methods are very popular since they lead to fairly easy implementation due to the formal marginalization of the infinite-dimensional parameter of the model. However, the straightforwardness of these methods also entails some limitations: they typically yield point estimates in the form of posterior expectations, but...

In this discussion we focus on density estimation and show that BNP models naturally provide a tool that is fairly stable under rescaling of the data. Müller and Mitra deal with the flexibility of BNP models and show, through some examples, that their use can be advantageous in common inference problems. As for density estimation, the paper describ...

In this paper we provide a mathematical reconstruction of what might have been Gauss' own derivation of the linking number of 1833, providing also an alternative, explicit proof of its modern interpretation in terms of degree, signed crossings and inter-section number. The reconstruction presented here is entirely based on an accurate study of Gaus...

In these lectures we present for the first time a mathematical reconstruction of what might have been Gauss’ own derivation of the linking number of 1833, and we provide also an alternative, explicit proof of its modern interpretation in terms of degree, signed crossings and intersection number. The reconstruction offered here is entirely based on...