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Prior Density Learning in Variational Bayesian Phylogenetic Parameters Inference
The advances in variational inference are providing promising paths in Bayesian estimation problems. These advances make variational phylogenetic...
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Sharp uniform-in-time propagation of chaos
We prove the optimal rate of quantitative propagation of chaos, uniformly in time, for interacting diffusions. Our main examples are interactions...
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On Decompositions of Complete 3-Uniform Hypergraphs into a Linear Forest with 4 Edges
A 3-uniform linear forest is any hypergraph obtained by starting with a single 3-uniform edge and adding other 3-uniform edges sequentially such that...
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An ε-Uniform Method for Singularly Perturbed 2D Convection Dominated Elliptic Boundary Value Problems with Delay and Advance
This article investigates linear two-dimensional elliptic singularly perturbed problems with delay and advance terms in both directions of the...
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An Adjusted Gray Map Technique for Constructing Large Four-Level Uniform Designs
A uniform experimental design (UED) is an extremely used powerful and efficient methodology for designing experiments with high-dimensional inputs,...
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ESR fractional model with non-zero uniform average blood velocity
This article discusses a new solution to the time-fractional ESR model, taking into account the non-zero uniform average blood velocity. We not only...
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Efficient Method for Solving the Boltzmann Equation on a Uniform Mesh
AbstractA new numerical method for solving the Boltzmann equation on a uniform mesh in velocity space is proposed. The asymptotic complexity of the...
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Uniform Convergence
In the previous two chapters we have seen what it means for a sequence...
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Variational inference in neural functional prior using normalizing flows: application to differential equation and operator learning problems
Physics-informed deep learning has recently emerged as an effective tool for leveraging both observational data and available physical laws....
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VI-DGP: A Variational Inference Method with Deep Generative Prior for Solving High-Dimensional Inverse Problems
Solving high-dimensional Bayesian inverse problems (BIPs) with the variational inference (VI) method is promising but still challenging. The main...
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Fitted Difference Scheme on a Non-uniform Mesh for Singularly Perturbed Parabolic Reaction–Diffusion with Large Negative Shift and Non-local Boundary Condition
In this paper, we study the numerical solution of singularly perturbed parabolic reaction–diffusion problems with large delay in space, and the right...
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Uniform large deviations for a class of semilinear stochastic partial differential equations driven by a Brownian sheet
We prove a uniform large deviation principle for the law of the solutions to a class of semilinear stochastic partial differential equations driven...
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A Practical Strategy for Valid Partial Prior-Dependent Possibilistic Inference
This paper considers statistical inference in contexts where only incomplete prior information is available. We develop a practical construction of a...
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Full recovery from point values: an optimal algorithm for Chebyshev approximability prior
Given pointwise samples of an unknown function belonging to a certain model set, one seeks in optimal recovery to recover this function in a way that...
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Constrained and unconstrained deep image prior optimization models with automatic regularization
Deep Image Prior (DIP) is currently among the most efficient unsupervised deep learning based methods for ill-posed inverse problems in imaging. This...
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6-Uniform Maker-Breaker Game is PSPACE-Complete
In a STOC 1976 paper, Schaefer proved that it is PSPACE-complete to determine the winner of the so-called Maker-Breaker game on a given set system,...
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A Unified Approach to Uniform Signal Recovery From Nonlinear Observations
Recent advances in quantized compressed sensing and high-dimensional estimation have shown that signal recovery is even feasible under strong...
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Prior Distribution Selection for a Mixture of Experts
AbstractThe paper investigates a mixture of expert models. The mixture of experts is a combination of experts, local approximation model, and a gate...
