Article
Operations Research & Management Science
Roberto Casarin, Bertrand B. Maillet, Anthony Osuntuyi
Summary: This article introduces two new stochastic optimization-based simulated annealing algorithms for addressing problems associated with statistical methods. These methods are effective in handling integral constrained optimization problems and show potential in financial applications.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Computer Science, Software Engineering
John Taylor Chavis, Amy Louise Cochran, Christopher James Earls
Summary: CU-MSDSp is a parallel RJMCMC implementation that aims to increase accessibility of RJMCMC to practitioners. It independently forms Markov Chains to approximate the posterior distribution of model parameters, and uses these approximations to estimate the posterior distribution of the model space. This software eliminates the need for designing a trans-dimensional proposal distribution, while ensuring the same theoretical guarantees as the non-parallel algorithm.
Article
Mathematics
Weijie Liu, Yan Shen, Lijuan Shen
Summary: This paper proposes a jump-diffusion model to measure the degradation of Lithium-ion batteries and uses probabilistic programming and Monte Carlo simulation to estimate model parameters and predict battery life. Simulation results and real data analysis demonstrate that this method can provide more accurate battery life predictions.
Article
Physics, Multidisciplinary
Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato
Summary: This study focuses on the incorporation of non-reversible dynamics, such as underdamped Langevin dynamics (ULD), into Langevin dynamics (LD) to accelerate mixing speed. The theoretical and numerical analysis clarifies important issues for practitioners, including selection criteria for skew-symmetric matrices, quantitative evaluations of acceleration, and large memory cost of storing skew matrices. A practical algorithm is presented to accelerate LD and ULD under parallel-chain Monte Carlo settings using memory-efficient skew-symmetric matrices.
Article
Statistics & Probability
Christophe Andrieu, Samuel Livingstone
Summary: Recent research has shown that comparison results for a type of nonreversible Monte Carlo Markov chains and processes can closely mirror those available in the reversible scenario, shedding light on earlier literature and strengthening some earlier results.
ANNALS OF STATISTICS
(2021)
Article
Optics
Yu Jiang, Manabu Machida, Norikazu Todoroki
Summary: Diffuse optical tomography uses near-infrared light and an iterative numerical scheme, with simulated annealing proposed as a method to find solutions even without good initial guesses. The proposed numerical method successfully reconstructs targets in the medium by finding the ground state of a spin Hamiltonian using simulated annealing.
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION
(2021)
Article
Chemistry, Multidisciplinary
Dwi Rantini, Nur Iriawan, Irhamah
Summary: The study applied the non-Gaussian RJMCMC (NG-RJMCMC) algorithm to analyze mixture models, transforming the Weibull distribution into extreme value (EV) type 1 (Gumbel-type) distribution for improved accuracy level, suitable for survival analysis. The EV-I mixture distribution showed higher coverage than the Gaussian mixture distribution in enzyme, acidity, and galaxy datasets, as well as in dengue hemorrhagic fever (DHF) data from Surabaya, Indonesia.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Physical
Philipp Hoellmer, A. C. Maggs, Werner Krauth
Summary: Event-chain Monte Carlo algorithms for hard-disk dipoles in two dimensions are benchmarked for potential applications in modeling water molecules. The rotation dynamics of dipoles are characterized through integrated autocorrelation times of polarization. The non-reversible event-driven ECMC algorithms show significant speedups compared to the Metropolis algorithm, with differences in speed observed among ECMC variants, indicating Newtonian ECMC as a promising solution for overcoming dynamical arrest in dipolar models with Coulomb interactions.
JOURNAL OF CHEMICAL PHYSICS
(2022)
Review
Automation & Control Systems
Venkat Anantharam
Summary: This paper studies discrete time reversible Markov decision processes with finite state and action spaces, and the simplification of the policy iteration algorithm in such problems, as well as the relation between the finite time evolution of reward accumulation and the Gaussian free field associated to the controlled Markov chain.
SYSTEMS & CONTROL LETTERS
(2022)
Article
Business, Finance
Xing Jin, Yi Hong
Summary: This paper examines tractable jump-diffusion models, particularly stochastic volatility models, for stock returns and variance processes. The study applies the Markov chain Monte Carlo (MCMC) method for model estimation and evaluates the models' ability to capture the term structure of variance swap rates and fit the dynamics of stock returns. The findings suggest that stochastic volatility models with self-exciting and linearly-dependent jumps in variance are consistent in estimating models, explaining stylized facts in variance swaps, and improving pricing performance. Empirical results indicate that infrequent self-exciting jumps in spot variance contribute to term structure modeling for variance swaps, while small-sized jumps in central-tendency variance signal substantial regime changes in the long run, particularly during market turmoils from 2008 to 2021.
INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS
(2023)
Article
Computer Science, Information Systems
Wilson Tsakane Mongwe, Rendani Mbuvha, Tshilidzi Marwala
Summary: Hybrid Monte Carlo (HMC) is commonly used in machine learning and statistics; it faces two main practical issues which are addressed through different methods; the A-S2HMC algorithm combines the advantages of NUTS and S2HMC, showing improved performance.
Article
Engineering, Mechanical
P. L. Green, L. J. Devlin, R. E. Moore, R. J. Jackson, J. Li, S. Maskell
Summary: This paper discusses the optimization of the 'L-kernel' in Sequential Monte Carlo samplers to improve performance, resulting in reduced variance of estimates and fewer resampling requirements.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Computer Science, Interdisciplinary Applications
DanHua ShangGuan
Summary: The Monte Carlo method is a powerful tool in many research fields, but the increasing complexity of physical models and mathematical models requires efficient algorithms to overcome the computational cost.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Engineering, Mechanical
Adolphus Lye, Alice Cicirello, Edoardo Patelli
Summary: This tutorial paper reviews the use of advanced Monte Carlo sampling methods in Bayesian model updating for engineering applications, introducing different methods and comparing their performance. Three case studies demonstrate the advantages and limitations of these sampling techniques in parameter identification, posterior distribution sampling, and stochastic identification of model parameters.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Physics, Fluids & Plasmas
Paul L. Ebert, Denis Gessert, Martin Weigel
Summary: This article introduces a Monte Carlo algorithm called population annealing, and expands it by incorporating weighted averaging, which can be applied to various measurements. The feasibility of this method is verified through numerical results, and an effective method for measuring spin overlaps is proposed.
Article
Geochemistry & Geophysics
Kai Gao, Eric T. Chung, Richard L. Gibson, Shubin Fu, Yalchin Efendiev
Article
Computer Science, Interdisciplinary Applications
Kai Gao, Shubin Fu, Richard L. Gibson, Eric T. Chung, Yalchin Efendiev
JOURNAL OF COMPUTATIONAL PHYSICS
(2015)
Article
Geochemistry & Geophysics
Eric T. Chung, Yalchin Efendiev, Richard L. Gibson, Wing Tat Leung
Article
Geochemistry & Geophysics
Yongchae Cho, Richard L. Gibson, Maria Vasilyeva, Yalchin Efendiev
Article
Geochemistry & Geophysics
Dehan Zhu, Richard Gibson
Article
Geochemistry & Geophysics
Sireesh Dadi, Richard L. Gibson, William W. Sager
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
(2018)
Article
Computer Science, Interdisciplinary Applications
Abdullah Fahad Alyabes, Richard L. Gibson
COMPUTERS & GEOSCIENCES
(2018)
Article
Geochemistry & Geophysics
Richard L. Gibson, Kai Gao, Eric Chung, Yalchin Efendiev
Article
Geochemistry & Geophysics
Yongchae Cho, Richard L. Gibson
Article
Geochemistry & Geophysics
Yongchae Cho, Richard L. Gibson, Hyunggu Jun, Changsoo Shin
Article
Geochemistry & Geophysics
Edith Sotelo, Marco Favino, Richard L. Gibson
Summary: The study investigates the application of the generalized finite-element method (GFEM) in simulating the acoustic wave equation, enhancing solution accuracy by adding user-defined enrichment functions to the standard finite element method (FEM). The combination of plane waves oriented in different directions and a time integration scheme with constant time step in GFEM is compared against the spectral-element method (SEM) for accuracy and efficiency in wave propagation simulations, with GFEM showing comparable results.
Article
Chemistry, Multidisciplinary
Milan Brankovic, Eduardo Gildin, Richard L. Gibson, Mark E. Everett
Summary: The development of seismic data compression methods aims to reduce data volume and increase interpretation accuracy in real-time monitoring, while also being used for data denoising and signal detection.
APPLIED SCIENCES-BASEL
(2021)
Article
Mathematics, Interdisciplinary Applications
Eric T. Chung, Yalchin Efendiev, Richard L. Gibson, Maria Vasilyeva
GEM-INTERNATIONAL JOURNAL ON GEOMATHEMATICS
(2016)
Proceedings Paper
Geochemistry & Geophysics
Richard L. Gibson, Kai Gao
FUNDAMENTAL CONTROLS ON FLUID FLOW IN CARBONATES: CURRENT WORKFLOWS TO EMERGING TECHNOLOGIES
(2015)