
Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction
Modern data analytics take advantage of ensemble learning and transfer l...
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A Unifying and Canonical Description of MeasurePreserving Diffusions
A complete recipe of measurepreserving diffusions in Euclidean space wa...
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A SelfSensing Digital Twin of a Railway Bridge using the Statistical Finite Element Method
The monitoring of infrastructure assets using sensor networks is becomin...
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Bayesian Assessments of Aeroengine Performance
Aeroengine performance is determined by temperature and pressure profile...
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Continuous calibration of a digital twin: comparison of particle filter and Bayesian calibration approaches
Assimilation of continuously streamed monitored data is an essential com...
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Integration in reproducing kernel Hilbert spaces of Gaussian kernels
The Gaussian kernel plays a central role in machine learning, uncertaint...
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SemiExact Control Functionals From Sard's Method
This paper focuses on the numerical computation of posterior expected qu...
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Convergence Guarantees for Gaussian Process Approximations Under Several Observation Models
Gaussian processes are ubiquitous in statistical analysis, machine learn...
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Burglary in London: Insights from Statistical Heterogeneous Spatial Point Processes
To obtain operational insights regarding the crime of burglary in London...
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Embedded Ridge Approximations: Constructing Ridge Approximations Over Localized Scalar Fields For Improved SimulationCentric Dimension Reduction
Many quantities of interest (qois) arising from differentialequationce...
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Multiresolution Multitask Gaussian Processes
We consider evidence integration from potentially dependent observation ...
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Minimum Stein Discrepancy Estimators
When maximum likelihood estimation is infeasible, one often turns to sco...
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Statistical Inference for Generative Models with Maximum Mean Discrepancy
While likelihoodbased inference and its variants provide a statisticall...
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The Statistical Finite Element Method
The finite element method (FEM) is one of the great triumphs of modern d...
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Stein Point Markov Chain Monte Carlo
An important task in machine learning and statistics is the approximatio...
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Hamiltonian Monte Carlo on Symmetric and Homogeneous Spaces via Symplectic Reduction
The Hamiltonian Monte Carlo method generates samples by introducing a me...
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Optimality Criteria for Probabilistic Numerical Methods
It is well understood that Bayesian decision theory and average case ana...
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Efficiency and robustness in Monte Carlo sampling of 3D geophysical inversions with Obsidian v0.1.2: Setting up for success
The rigorous quantification of uncertainty in geophysical inversions is ...
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The synthesis of data from instrumented structures and physicsbased models via Gaussian processes
A recent development which is poised to disrupt current structural engin...
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Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"
This article is the rejoinder for the paper "Probabilistic Integration: ...
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A RiemannianStein Kernel Method
This paper presents a theoretical analysis of numerical integration base...
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Probabilistic Linear Solvers: A Unifying View
Several recent works have developed a new, probabilistic interpretation ...
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Posterior Inference for Sparse Hierarchical Nonstationary Models
Gaussian processes are valuable tools for nonparametric modelling, wher...
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A Bayesian Conjugate Gradient Method
A fundamental task in numerical computation is the solution of large lin...
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Bayesian Quadrature for Multiple Related Integrals
Bayesian probabilistic numerical methods are a set of tools providing po...
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Posterior Integration on a Riemannian Manifold
The geodesic Markov chain Monte Carlo method and its variants enable com...
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How Deep Are Deep Gaussian Processes?
Recent research has shown the potential utility of probability distribut...
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A determinantfree method to simulate the parameters of large Gaussian fields
We propose a determinantfree approach for simulationbased Bayesian inf...
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On the Sampling Problem for Kernel Quadrature
The standard Kernel Quadrature method for numerical integration with ran...
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Geometry and Dynamics for Markov Chain Monte Carlo
Markov Chain Monte Carlo methods have revolutionised mathematical comput...
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Bayesian Probabilistic Numerical Methods
The emergent field of probabilistic numerics has thus far lacked clear s...
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Probabilistic Numerical Methods for PDEconstrained Bayesian Inverse Problems
This paper develops meshless methods for probabilistically describing di...
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On the Geometric Ergodicity of Hamiltonian Monte Carlo
We establish general conditions under which Markov chains produced by th...
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Probabilistic Integration: A Role in Statistical Computation?
A research frontier has emerged in scientific computation, wherein numer...
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FrankWolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
There is renewed interest in formulating integration as an inference pro...
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Probabilistic Numerics and Uncertainty in Computations
We deliver a call to arms for probabilistic numerical methods: algorithm...
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Unbiased Bayes for Big Data: Paths of Partial Posteriors
A key quantity of interest in Bayesian inference are expectations of fun...
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PseudoMarginal Bayesian Inference for Gaussian Processes
The main challenges that arise when adopting Gaussian Process priors in ...
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On Russian Roulette Estimates for Bayesian Inference with DoublyIntractable Likelihoods
A large number of statistical models are "doublyintractable": the likel...
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