Metropolis-adjusted langevin algorithm

Results 61 to 70 of about 2,849 (140)

Bayesian backcalculation of pavement properties using parallel transitional Markov chain Monte Carlo

Computer-Aided Civil and Infrastructure Engineering, Volume 39, Issue 13, Page 1911-1927, 1 July 2024.
Abstract This paper presents a novel Bayesian method for backcalculation of pavement dynamic modulus, stiffness, thickness, and damping using falling weight deflectometer (FWD) data. The backcalculation procedure yields estimates and uncertainties for each pavement property of interest.
Keaton Coletti, Ryan C. Romeo, R. Benjamin Davis +2 more
wiley +1 more source

An Adaptive Version for the Metropolis Adjusted Langevin Algorithm with a Truncated Drift [PDF]

This paper proposes an adaptive version for the Metropolis adjusted Langevin algorithm with a truncated drift (T-MALA). The scale parameter and the covariance matrix of the proposal kernel of the algorithm are simultaneously and recursively updated in ...
Yves Atchade
core

Development of a bottom‐up coarse‐grained model for interactions of lipids with TiO 2 nanoparticles

Journal of Computational Chemistry, Volume 45, Issue 16, Page 1364-1379, June 15, 2024.
Computational methods have become an important tool in studying the mechanisms of nanomaterial exposure. However, while the simulations of nanoparticles interacting with biomatter at atomic resolution are exceptionally detailed, computing power becomes a bottleneck with the increasing size of simulated systems. Here we present a coarse‐grained model of
Mikhail Ivanov, Alexander P. Lyubartsev
wiley +1 more source

Limit theorems for some adaptive MCMC algorithms with subgeometric kernels: Part II

, 2008
We prove a central limit theorem for a general class of adaptive Markov Chain Monte Carlo algorithms driven by sub-geometrically ergodic Markov kernels. We discuss in detail the special case of stochastic approximation.
Atchade, Yves F., Fort, Gersende
core +5 more sources

Intelligent Structure Searching and Designs for Nanoclusters: Effective Units in Atomic Manufacturing

Advanced Intelligent Systems, Volume 6, Issue 6, June 2024.
The present cluster search methods and challenges currently faced have been summarized. The rapid development of big data‐driven machine learning is expected to solve some practical problems including the structural and properties evolution of clusters in complex environment. Clusters, an aggregation of several to thousands of atoms, molecules, or ions,
Junfeng Gao +7 more
wiley +1 more source

Cell Shape and Forces in Elastic and Structured Environments: From Single Cells to Organoids

Advanced Functional Materials, Volume 34, Issue 20, May 16, 2024.
In this review, it is described how methods from materials science are being used to understand and control the behavior of biological cells in elastic and structured environments. This field currently moves from single cells to cell collectives and organoids, and from 2D to 3D environments.
Rabea Link +4 more
wiley +1 more source

Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

Journal of Extracellular Vesicles, Volume 13, Issue 2, February 2024.
Abstract Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year‐on‐year increase in the numbers of scientific ...
Joshua A. Welsh +1125 more
wiley +1 more source

Hamiltonian ABC

, 2015
Approximate Bayesian computation (ABC) is a powerful and elegant framework for performing inference in simulation-based models. However, due to the difficulty in scaling likelihood estimates, ABC remains useful for relatively low-dimensional problems. We
Leenders, Robert, Meeds, Edward, Welling, Max +2 more
core

Convergence Rates for the Constrained Sampling via Langevin Monte Carlo. [PDF]

Entropy (Basel), 2023
Zhu Y.
europepmc +1 more source

Variational Hybrid Monte Carlo for Efficient Multi-Modal Data Sampling. [PDF]

Entropy (Basel), 2023
Sun S, Zhao J, Gu M, Wang S.
europepmc +1 more source

algorithm
mathematics
markov chain monte carlo

statistics
langevin dynamics
computer science

metropolis–hastings algorithm
physics
statistical physics