Article
Energy & Fuels
Jiaqiao Li, Guobang Wang, Tiancheng Tang, Jinjin Fan, Shengyuan Liu, Zhenzhi Lin
Summary: This paper proposes an optimization strategy for distribution network reconfiguration based on the Markov chain Monte Carlo method, which achieves peak load shaving and load balancing of low voltage distribution lines. By using load characteristic curve clustering and optimization model, the line load rate and resource utilization are improved.
Article
Biochemical Research Methods
Zhuoran Ding, Marylyn D. Ritchie, Benjamin F. Voight, Wei-Ting Hwang
Summary: This study identifies causal factors for complex traits in humans using observational studies and Mendelian randomization experiments, and infers the effect of hidden mediators on the outcome trait through a mediation model framework. Simulation and data analysis show the effectiveness of the proposed method.
BMC BIOINFORMATICS
(2022)
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
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
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
Environmental Sciences
Jonas Allgeier, Olaf A. A. Cirpka
Summary: Modern physics-based subsurface-flow models often require many parameters and computationally costly simulations. To expedite the calibration process, we propose using surrogate models based on Gaussian Process Regression (GPR), which allows estimation of the posterior parameter distribution using Markov-Chain Monte Carlo (MCMC) simulations. We compared the GPR-based approach to a Neural Posterior Estimation (NPE) scheme and found that the GPR-based MCMC approach reproduced the data better.
WATER RESOURCES RESEARCH
(2023)
Article
Statistics & Probability
Xiaotian Zheng, Athanasios Kottas, Bruno Sanso
Summary: The study introduces a framework for constructing stationary MTD models that extend beyond linear, Gaussian dynamics. Conditions for first-order strict stationarity are explored, with inference and prediction developed under the Bayesian framework with structured priors for mixture weights. Model properties are investigated analytically and via synthetic data examples, with real data applications illustrating Poisson and Lomax examples.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2022)
Article
Mathematics, Applied
Hillary R. Fairbanks, Umberto Villa, Panayot S. Vassilevski
Summary: This work introduces a new hierarchical multilevel method for generating Gaussian random field realizations in a scalable manner, which is tested in a multilevel MCMC algorithm to explore its feasibility.
SIAM JOURNAL ON SCIENTIFIC COMPUTING
(2021)
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
Mathematics
Johny Pambabay-Calero, Sergio Bauz-Olvera, Ana Nieto-Librero, Ana Sanchez-Garcia, Puri Galindo-Villardon
Summary: Models implemented in statistical software for precision analysis of diagnostic tests include random-effects modeling and hierarchical regression, but calculating the covariance between sensitivity and specificity is challenging when the random effect is zero. Copulas are used as an alternative method, and posterior values are estimated using Markov chain Monte Carlo methods.
Article
Statistics & Probability
Hai-Dang Dau, Nicolas Chopin
Summary: The paper proposes a new waste-free sequential Monte Carlo (SMC) algorithm that utilizes the outputs of all intermediate Markov chain Monte Carlo (MCMC) steps as particles. The consistency and asymptotic normality of its output are established, and insights on estimating the asymptotic variance of any particle estimate are developed. Empirical results show that waste-free SMC tends to outperform standard SMC samplers, particularly in scenarios where the mixing of the considered MCMC kernels decreases across iterations.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
(2022)
Article
Physics, Multidisciplinary
Hanqing Zhao, Marija Vucelja
Summary: We introduce an efficient nonreversible Markov chain Monte Carlo algorithm for generating self-avoiding walks with a variable endpoint, and compare its performance with existing algorithms in two and three dimensions.
FRONTIERS IN PHYSICS
(2022)
Article
Mathematics, Applied
Tengchao Yu, Hongqiao Wang, Jinglai Li
Summary: The paper introduces a design criterion based on KSE for optimizing algorithm parameters of HMC sampler, especially when the mass matrix is adapted. Analytically derivations of optimal algorithm parameters for near-Gaussian distributions are provided, as well as theoretical justification for adapting mass matrix in HMC sampler. An adaptive HMC algorithm is proposed and its performance demonstrated with numerical examples.
SIAM JOURNAL ON SCIENTIFIC COMPUTING
(2021)
Article
Automation & Control Systems
Jianqing Fan, Bai Jiang, Qiang Sun
Summary: This paper establishes Hoeffding's lemma and inequality for bounded functions of general-state space and not necessarily reversible Markov chains, showing the necessity of boundedness of functions for such results. The optimality of the ratio between variance proxies in the Markov-dependent and independent settings is characterized. The new results are applied to various practical problems to showcase their usefulness.
JOURNAL OF MACHINE LEARNING RESEARCH
(2021)
Article
Thermodynamics
Emerson L. Sanches, Diego C. Knupp, Leonardo T. Stutz, Luiz A. S. Abreu, Antonio J. Silva Neto
Summary: This work experimentally validates the explicit estimation of time-varying boundary heat fluxes in thermally thin plates using temperature measurements obtained by infrared thermography. Regularizing the measurements with controlled truncation of eigenfunction expansions overcomes the ill-posed nature of the inverse problem. Reference results obtained from Bayesian inference demonstrate the feasibility of the explicit technique for practical applications with minimal computational effort.
THERMAL SCIENCE AND ENGINEERING PROGRESS
(2021)
Article
Statistics & Probability
Nicolas Chopin, Sumeetpal S. Singh
Review
Statistics & Probability
Mathieu Gerber, Nicolas Chopin
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
(2015)
Article
Statistics & Probability
Chris J. Oates, Mark Girolami, Nicolas Chopin
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
(2017)
Article
Statistics & Probability
Nicolas Chopin, James Ridgway
STATISTICAL SCIENCE
(2017)
Article
Statistics & Probability
Alexander Buchholz, Nicolas Chopin
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2019)
Article
Statistics & Probability
Mathieu Gerber, Nicolas Chopin, Nick Whiteley
ANNALS OF STATISTICS
(2019)
Article
Physics, Multidisciplinary
Nicolas Chopin, Gabriel Ducrocq
Summary: Cube thinning is a novel method for compressing the output of an MCMC algorithm with control variates, allowing for resampling of the initial sample based on weights derived from the control variates. The advantage of cube thinning is that its complexity is independent of the size of the compressed sample, unlike previous methods such as Stein thinning.
Article
Statistics & Probability
Hai-Dang Dau, Nicolas Chopin
Summary: The paper proposes a new waste-free sequential Monte Carlo (SMC) algorithm that utilizes the outputs of all intermediate Markov chain Monte Carlo (MCMC) steps as particles. The consistency and asymptotic normality of its output are established, and insights on estimating the asymptotic variance of any particle estimate are developed. Empirical results show that waste-free SMC tends to outperform standard SMC samplers, particularly in scenarios where the mixing of the considered MCMC kernels decreases across iterations.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
(2022)
Article
Psychology, Biological
Charles Findling, Nicolas Chopin, Etienne Koechlin
Summary: Research suggests that simple, low-level inferences about uncertainty may be more effective in developing adaptive behavior in volatile environments than complex computations or high-level inferences about volatility. The Weber-imprecision model, based on imprecise representations of uncertainty, demonstrates near-optimal adaptive behavior regardless of environmental volatility and fits human behavior better than other optimal adaptive models.
NATURE HUMAN BEHAVIOUR
(2021)
Proceedings Paper
Statistics & Probability
Danilo Alvares, Carmen Armero, Anabel Forte, Nicolas Chopin
BAYESIAN STATISTICS IN ACTION, BAYSM 2016
(2017)
Article
Statistics & Probability
Mathieu Gerber, Nicolas Chopin
Proceedings Paper
Mathematics
Colas Schretter, Zhijian He, Mathieu Gerber, Nicolas Chopin, Harald Niederreiter
MONTE CARLO AND QUASI-MONTE CARLO METHODS
(2016)
Proceedings Paper
Engineering, Electrical & Electronic
Nicolas Chopin, Mathieu Gerber
2015 23RD EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)
(2015)
Article
Statistics & Probability
Lionel Riou-Durand, Nicolas Chopin
ELECTRONIC JOURNAL OF STATISTICS
(2018)
Article
Automation & Control Systems
Pierre Alquier, James Ridgway, Nicolas Chopin
JOURNAL OF MACHINE LEARNING RESEARCH
(2016)