Spatiotemporal wildfire modeling through point processes with moderate and extreme marks

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The main aim of this thesis is to study extreme value theory in a multivariate framework and to propose new methods for modeling and estimating dependence between several variables at extreme levels. We first give a brief overview of the main concepts of e ...

The conservation and restoration of forest ecosystems require detailed knowledge of the native plant compositions. Here, we map global forest tree composition and assess the impacts of historical forest cover loss and climate change on trees. The global oc ...

Glacier-fed streams are the cold, ultra-oligotrophic, and unstable streams that are fed by glacial meltwater. Despite these extreme conditions, they harbour a diverse and abundant microbial diversity that develops into biofilms, covering the boulders and s ...

Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier f ...

In this work, we present, analyze, and implement a class of multilevel Markov chain Monte Carlo(ML-MCMC) algorithms based on independent Metropolis--Hastings proposals for Bayesian inverse problems. In this context, the likelihood function involves solving ...

Local and global inference methods have been developed to infer structural contacts from multiple sequence alignments of homologous proteins. They rely on correlations in amino acid usage at contacting sites. Because homologous proteins share a common ance ...

This thesis presents work at the junction of statistics and climate science. We first provide methodology for use by climate scientists when performing fast event attribution using extreme value theory, and then describe two interdisciplinary projects in c ...

Higher-order asymptotics provide accurate approximations for use in parametric statistical modelling. In this thesis, we investigate using higher-order approximations in two-specific settings, with a particular emphasis on the tangent exponential model. ...

We present an extended validation of semi-analytical, semi-empirical covariance matrices for the two-point correlation function (2PCF) on simulated catalogs representative of luminous red galaxies (LRGs) data collected during the initial 2 months of operat ...

This paper introduces a new modeling and inference framework for multivariate and anisotropic point processes. Building on recent innovations in multivariate spatial statistics, we propose a new family of multivariate anisotropic random fields, and from th ...

We study the problem of drift estimation for two-scale continuous time series. We set ourselves in the framework of overdamped Langevin equations, for which a single-scale surrogate homogenized equation exists. In this setting, estimating the drift coeffic ...

Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive p ...

The increasing integration of intermittent renewable generation, especially at the distribution level, necessitates advanced planning and optimisation methodologies contingent on the knowledge of the admittance matrix, capturing the topology and line param ...

We study the computations that Bayesian agents undertake when exchanging opinions over a network. The agents act repeatedly on their private information and take myopic actions that maximize their expected utility according to a fully rational posterior be ...

Environmental extreme events can have devastating impacts on society when they interact with vulnerable human and natural systems. Such events can result from natural causes, like phenomena related to the El Ni~no-Southern Oscillation or decadal/multi-dec ...

Aims. We investigate the contribution of shot-noise and sample variance to uncertainties in the cosmological parameter constraints inferred from cluster number counts, in the context of the Euclid survey. ...

This thesis is devoted to the construction, analysis, and implementation of two types of hierarchical Markov Chain Monte Carlo (MCMC) methods for the solution of large-scale Bayesian Inverse Problems (BIP). The first hierarchical method we present is base ...

In this thesis we explore uncertainty quantification of forward and inverse problems involving differential equations. Differential equations are widely employed for modeling natural and social phenomena, with applications in engineering, chemistry, meteor ...

The application of Bayesian modeling techniques is increasingly common in neuroscience due to the coherent and principled way in which the paradigm deals with uncertainty. The Bayesian framework is particularly valuable in the context of complex, ill-posed ...

Set-point viral load (SPVL), a common measure of human immunodeficiency virus (HIV)-1 virulence, is partially determined by viral genotype. Epidemiological evidence suggests that this viral property has been under stabilising selection, with a typical opti ...

Spatiotemporal wildfire modeling through point processes with moderate and extreme marks

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Flexible Statistical Inference for Multivariate Extremes

Regional uniqueness of tree species composition and response to forest loss and climate change

The microbial genomics of glacier-fed streams: adaptations to an extreme ecosystem

Robust Outlier Rejection for 3D Registration with Variational Bayes

Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings

Impact of phylogeny on structural contact inference from protein sequence data

Wind, Hail, and Climate Extremes: Modelling and Attribution Studies for Environmental Data

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Validation of semi-analytical, semi-empirical covariance matrices for two-point correlation function for early DESI data

Multivariate geometric anisotropic Cox processes

Drift Estimation of Multiscale Diffusions Based on Filtered Data

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Bayesian Methods for the Identification of Distribution Networks

Bayesian Decision Making in Groups is Hard

Spatiotemporal modelling of extreme wildfires and severe thunderstorm environments

Euclid : Effects of sample covariance on the number counts of galaxy clusters

HIERARCHICAL MARKOV CHAIN MONTE CARLO METHODS FOR BAYESIAN INVERSE PROBLEMS

Probabilistic and Bayesian methods for uncertainty quantification of deterministic and stochastic differential equations

Bayesian modeling of brain function and behavior

Phylogenetic estimation of the viral fitness landscape of HIV-1 set-point viral load