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To synthesize diffusion MR measurements from Monte-Carlo simulation using tissue models with sizes comparable to those of scan voxels. Larger regions enable restricting structures to be modeled in greater detail and improve accuracy and…

Computational Physics · Physics 2017-01-16 Matt G Hall , Gemma Nedjati-Gilani , Daniel C Alexander

Dynamic treatment regimens (DTRs), also known as treatment algorithms or adaptive interventions, play an increasingly important role in many health domains. DTRs are motivated to address the unique and changing needs of individuals by…

Markov Chain Monte Carlo (MCMC) is a well-established family of algorithms primarily used in Bayesian statistics to sample from a target distribution when direct sampling is challenging. Existing work on Bayesian decision trees uses MCMC.…

Computation · Statistics 2023-01-24 Efthyvoulos Drousiotis , Paul G. Spirakis , Simon Maskell

In computed tomography (CT) reconstruction, scattering causes server quality degradation of the reconstructed CT images by introducing streaks and cupping artifacts which reduce the detectability of low contrast objects. Monte Carlo (MC)…

Image and Video Processing · Electrical Eng. & Systems 2022-02-01 Ammar Alsaffar , Steffen Kieß , Kaicong Sun , Sven Simon

Bootstrapping was designed to randomly resample data from a fixed sample using Monte Carlo techniques. However, the original sample itself defines a discrete distribution. Convolutional methods are well suited for discrete distributions,…

Methodology · Statistics 2021-07-19 Jared M. Clark , Richard L. Warr

Efficient sampling from high-dimensional distributions is a challenging issue which is encountered in many large data recovery problems involving Markov chain Monte Carlo schemes. In this context, sampling using Hamiltonian dynamics is one…

Methodology · Statistics 2015-02-02 Lotfi Chaari , Jean-Yves Tourneret , Caroline Chaux , Hadj Batatia

An optimal data partitioning in parallel & distributed implementation of clustering algorithms is a necessary computation as it ensures independent task completion, fair distribution, less number of affected points and better & faster…

Artificial Intelligence · Computer Science 2016-09-21 Saraswati Mishra , Avnish Chandra Suman

We introduce an efficient numerical implementation of a Markov Chain Monte Carlo method to sample a probability distribution on a manifold (introduced theoretically in Zappa, Holmes-Cerfon, Goodman (2018)), where the manifold is defined by…

Computation · Statistics 2023-08-22 Kerun Xu , Miranda Holmes-Cerfon

Classical algorithms in numerical analysis for numerical integration (quadrature/cubature) follow the principle of approximate and integrate: the integrand is approximated by a simple function (e.g. a polynomial), which is then integrated…

Numerical Analysis · Mathematics 2018-06-15 Yuji Nakatsukasa

High-dimensional multimodal sampling problems from lattice field theory (LFT) have become important benchmarks for machine learning assisted sampling methods. We show that GPU-accelerated particle methods, Sequential Monte Carlo (SMC) and…

Machine Learning · Statistics 2025-11-20 David Yallup

Existing variance reduction techniques used in stochastic simulations for rare event analysis still require a substantial number of model evaluations to estimate small failure probabilities. In the context of complex, nonlinear finite…

Machine Learning · Computer Science 2025-08-04 Liuyun Xu , Seymour M. J. Spence

We describe a novel simulation method that eliminates the slowing-down problem in the Monte Carlo simulations of imaginary-time path integrals near the continuum limit. This method combines a stochastic blocking procedure with the multigrid…

Statistical Mechanics · Physics 2007-05-23 C. H. Mak , Sergei Zakharov

Monte Carlo radiative transfer, which has been demonstrated as a successful algorithm for modeling radiation transport through the astrophysical medium, relies on sampling of scattering phase functions. We review several classic sampling…

Computational Physics · Physics 2019-09-18 Jianing Zhang

Multifidelity Monte Carlo methods often rely on a preprocessing phase consisting of standard Monte Carlo sampling to estimate correlation coefficients between models of different fidelity to determine the weights and number of samples for…

Data Analysis, Statistics and Probability · Physics 2021-06-29 Todd A. Oliver , Christopher S. Simmons , Robert D. Moser

To better understand the capture process by a nanopore, we introduce an efficient Kinetic Monte Carlo (KMC) algorithm that can simulate long times and large system sizes by mapping the dynamic of a point-like particle in a 3D spherically…

Biological Physics · Physics 2021-03-22 Le Qiao , Maxime Ignacio , Gary W. Slater

Elastic systems that are spatially heterogeneous in their mechanical response pose special challenges for molecular simulations. Standard methods for sampling thermal fluctuations of a system's size and shape proceed through a series of…

Materials Science · Physics 2015-05-13 Sander Pronk , Phillip L. Geissler

We develop a machine learning algorithm to turn around stratification in Monte Carlo sampling. We use a different way to divide the domain space of the integrand, based on the height of the function being sampled, similar to what is done in…

High Energy Physics - Phenomenology · Physics 2024-12-19 Kayoung Ban , Myeonghun Park , Raymundo Ramos

Due to the intrinsic complexity of the quantum many-body problem, quantum Monte Carlo algorithms and their corresponding Monte Carlo configurations can be defined in various ways. Configurations corresponding to few Feynman diagrams often…

Strongly Correlated Electrons · Physics 2019-04-30 Alexander Kowalski , Andreas Hausoel , Markus Wallerberger , Patrik Gunacker , Giorgio Sangiovanni

We investigate the use of a stratified sampling approach for LIME Image, a popular model-agnostic explainable AI method for computer vision tasks, in order to reduce the artifacts generated by typical Monte Carlo sampling. Such artifacts…

Artificial Intelligence · Computer Science 2024-03-27 Muhammad Rashid , Elvio G. Amparore , Enrico Ferrari , Damiano Verda

Machine learning models are commonly applied to human brain imaging datasets in an effort to associate function or structure with behaviour, health, or other individual phenotypes. Such models often rely on low-dimensional maps generated by…

Quantitative Methods · Quantitative Biology 2021-09-21 Gregory Kiar , Yohan Chatelain , Ali Salari , Alan C. Evans , Tristan Glatard