An introduction to MCMC for machine learning.

Maximum Likelihood from Incomplete Data Via the EM Algorithm

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Optimization by Simulated Annealing

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Equation of State Calculations by Fast Computing Machines

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Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.

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Monte Carlo sampling methods using Markov chains and their applications.

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Markov Chain Monte Carlo in Practice

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Novel approach to nonlinear/non-Gaussian Bayesian state estimation

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Sequential Monte Carlo Methods in Practice

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Sampling-based approaches to calculating marginal densities

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A Survey of Parametric Static Analysis

Park, J., Lee, H., & Ryu, S. (2021) · ACM Computing Surveys · doi:10.1145/3464457 · Cited by 20

A case study of using x-ray Thomson scattering to diagnose the in-flight plasma conditions of DT cryogenic implosions

Poole, H., Cao, D., Epstein, R., Golovkin, I., Walton, T., Hu, S. X., Kasim, M., Vinko, S. M., Rygg, J. R., Goncharov, V. N., et al. (2022) · Physics of Plasmas · doi:10.1063/5.0072790 · Cited by 19

Options for Prognostics Methods: A review of data-driven and physics- based prognostics

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Evaluation and improvement of global pose estimation with multiple AprilTags for industrial manipulators

Nissler, C., Buttner, S., Márton, Z.-C., Beckmann, L., & Thomasy, U. (2016) · IEEE International Conference on Emerging Technologies and Factory Automation · doi:10.1109/etfa.2016.7733711 · Cited by 25

Exact Sampling with Integer Linear Programs and Random Perturbations

Kim, C., Sabharwal, A., & Ermon, S. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10421 · Cited by 19

Mining Overlapping Communities and Inner Role Assignments through Bayesian Mixed-Membership Models of Networks with Context-Dependent Interactions

Costa, G., & Ortale, R. (2018) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3106368 · Cited by 19

An IRL Approach for Cyber-Physical Attack Intention Prediction and Recovery

Elnaggar, M. M., & Bezzo, N. (2018) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.2018.8430922 · Cited by 19

Learning dynamic Bayesian networks from time-dependent and time-independent data: Unraveling disease progression in Amyotrophic Lateral Sclerosis

Leão, T., Madeira, S. C., Gromicho, M., de Carvalho, M. A., & Carvalho, A. M. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103730 · Cited by 20

Spatial Processes for Recommender Systems

Fabian Bohnert, A., Schmidt, D. F., & Ingrid Zukerman, A. (2022) · doi:10.26180/20365248 · Cited by 19

Application of Bayesian Inference to Milling Force Modeling

Karandikar, J. M., Schmitz, T. L., & Abbas, A. E. (2014) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.4026365 · Cited by 19

Bayesian sensitivity analysis of bifurcating nonlinear models

Becker, W. E., Keith Worden, B., & Rowson, J. (2012) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2012.05.010 · Cited by 19

Capturing variability in pavement performance models from sufficient time-series predictors: a case study of the New Brunswick road network

Amador-Jiménez, L. E., & Mrawira, D. M. (2011) · Canadian Journal of Civil Engineering · doi:10.1139/l10-127 · Cited by 19

Explicit Tracking of Uncertainty Increases the Power of Quantitative Rule-of-Thumb Reasoning in Cell Biology

Johnston, I. G., Rickett, B. C., & Jones, N. S. (2014) · Biophysical Journal · doi:10.1016/j.bpj.2014.08.040 · Cited by 19

A hierarchical feature fusion framework for adaptive visual tracking

Makris, A., Kosmopoulos, D., Perantonis, S. J., & Theodoridis, S. (2011) · Image and Vision Computing · doi:10.1016/j.imavis.2011.07.001 · Cited by 19

Bayesian parameter estimation for biochemical reaction networks using region-based adaptive parallel tempering

Ballnus, B., Schaper, S., Theis, F. J., & Hasenauer, J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty229 · Cited by 19

Quantitative Health Risk Analysis Methods

Cox, L. A. (2006) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/b136472 · Cited by 20

A Sampling-Based Approach to Computing Equilibria in Succinct Extensive-Form GamesPreprint

Dudı́k, M., & Gordon, G. J. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1205.2649 · Cited by 19

Integrated optimal design for hybrid electric vehicles

Silvaş, E. (2015) · TU/e Research Portal · Cited by 19

On sequential Monte Carlo, partial rejection control and approximate Bayesian computationPreprint

Peters, G. W., Fan, Y., & Sisson, S. A. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0808.3466 · Cited by 19

Hypermodels for ExplorationPreprint

Dwaracherla, V., Lu, X., Ibrahimi, M., Osband, I., Wen, Z., & Roy, B. V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.07464 · Cited by 19

Scalable Computations of Wasserstein Barycenter via Input Convex Neural NetworksPreprint

Fan, J., Taghvaei, A., & Chen, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.04462 · Cited by 19

Probabilistic Approaches to Recommendations

Barbieri, N., Manco, G., & Ritacco, E. (2014) · Synthesis lectures on data mining and knowledge discovery · doi:10.1007/978-3-031-01906-7 · Cited by 18

Parameter estimation and uncertainty quantification using information geometry

Sharp, J. A., Browning, A. P., Burrage, K., & Simpson, M. J. (2022) · Journal of The Royal Society Interface · doi:10.1098/rsif.2021.0940 · Cited by 18

Enriching stochastic model updating metrics: An efficient Bayesian approach using Bray-Curtis distance and an adaptive binning algorithm

Zhao, W., Yang, L., Dang, C., Rocchetta, R., Valdebenito, M. A., & Moens, D. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.108889 · Cited by 18

Crucial control on magmatic-hydrothermal Sn deposit in the Tengchong block, SW China: Evidence from magma differentiation and zircon geochemistry

Zhang, Q., Wang, Q., Li, G., Sun, X. W., Shu, Q., & Deng, J. (2022) · Geoscience Frontiers · doi:10.1016/j.gsf.2022.101401 · Cited by 18

Joint reconstructions of growth and expansion histories from stage-IV surveys with minimal assumptions: Dark energy beyond Λ

Calderón, R., L’Huillier, B., Polarski, D., Shafieloo, A., & Starobinsky, A. A. (2022) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.106.083513 · Cited by 18

Identification of Influence Lines for Highway Bridges Using Bayesian Parametric Estimation Based on Computer Vision Measurements

Zhou, Y., Hu, J.-N., Hao, G., Zhu, Z.-R., & Zhang, J. (2023) · Journal of Bridge Engineering · doi:10.1061/jbenf2.beeng-6235 · Cited by 18

The Stochastic Search Dynamics of Interneuron Migration

Britto, J. M., Johnston, L. A., & Tan, S. (2009) · Biophysical Journal · doi:10.1016/j.bpj.2009.04.064 · Cited by 18

A learning based hierarchical model for vessel segmentation

Socher, R., Barbu, A., & Comaniciu, D. (2008) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2008.4541181 · Cited by 18

Markov chain Monte Carlo sampling based terahertz holography image denoising

Chen, G., & Li, Q. (2015) · Applied Optics · doi:10.1364/ao.54.004345 · Cited by 18

Variational inference for medical image segmentation

Blaiotta, C., Cardoso, M. J., & Ashburner, J. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.04.004 · Cited by 18

A ranking method for sensor services based on estimation of service access cost

Wang, W., Yao, F., De, S., Moessner, K., & Sun, Z. (2015) · Information Sciences · doi:10.1016/j.ins.2015.05.029 · Cited by 18

Discovering recurring patterns in electrophysiological recordings

Gips, B., Bahramisharif, A., Lowet, E., Roberts, M. J., Weerd, P. D., Jensen, O. B., & van der Eerden, J. P. J. M. (2016) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2016.11.001 · Cited by 18

Noise-dependent ranking of prognostics algorithms based on discrepancy without true damage information

Wang, Y., Gogu, C., Kim, N. H., Haftka, R. T., Binaud, N., & Bès, C. (2017) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2017.09.021 · Cited by 18

Exploring coral reef responses to millennial-scale climatic forcings: insights from the 1-D numerical tool pyReef-Core v1.0

Salles, T., Pall, J., Webster, J. M., & Dechnik, B. (2018) · Geoscientific model development · doi:10.5194/gmd-11-2093-2018 · Cited by 20

Gradient‐Based Inverse Estimation for a Rainfall‐Runoff Model

Krapu, C., Borsuk, M. E., & Kumar, M. (2019) · Water Resources Research · doi:10.1029/2018wr024461 · Cited by 18

Uncertainty Quantification for Parameter Estimation and Response Prediction

Ricciardi, D. E., Chkrebtii, O., & Niezgoda, S. R. (2019) · Integrating materials and manufacturing innovation · doi:10.1007/s40192-019-00154-3 · Cited by 18

Document Clustering Meets Topic Modeling with Word Embeddings

Costa, G., & Ortale, R. (2020) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611976236.28 · Cited by 18

Partially Observable Markov Decision Process for Monitoring Multilayer Wafer Fabrication

Khakifirooz, M., Fathi, M., & Chien, C. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.3017481 · Cited by 18

Advances in Machine and Deep Learning for Modeling and Real-Time Detection of Multi-messenger Sources

Huerta, E. A., & Zhao, Z. (2021) · Springer eBooks · doi:10.1007/978-981-15-4702-7_47-1 · Cited by 20

Bayesian Neural Networks for Identification and Classification of Radio Frequency Transmitters Using Power Amplifiers’ Nonlinearity Signatures

Xu, J., Shen, Y., Chen, E., & Chen, V. (2021) · IEEE Open Journal of Circuits and Systems · doi:10.1109/ojcas.2021.3089499 · Cited by 18

Psychophysiological Response to Acoustic Intensity Change in a Musical Chord

Olsen, K. N., & Stevens, C. (2013) · Journal of Psychophysiology · doi:10.1027/0269-8803/a000082 · Cited by 21

Adaptive Stochastic Resource Control: A Machine Learning Approach

Csáji, B. C., & Monostori, L. (2008) · Cited by 18

Composable probabilistic inference with b(laise)

Winston, P. H., Tenenbaum, J. B., & Bonawitz, K. (2008) · Cited by 19

Building fast Bayesian computing machines out of intentionally stochastic, digital partsPreprint

Mansinghka, V. K., & Jonas, E. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1402.4914 · Cited by 22

Forest point processes for the automatic extraction of networks in raster data

Schmidt, A., Lafarge, F., Brenner, C., Rottensteiner, F., & Heipke, C. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.01.012 · Cited by 21

A Bayesian approach for quantile optimization problems with high-dimensional uncertainty sources

Sabater, C., Maître, O. P. L., Congedo, P. M., & Görtz, S. (2021) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2020.113632 · Cited by 17

Optimizing Video Caching at the Edge: A Hybrid Multi-Point Process Approach

Zhang, X., Zhou, Y., Wu, D., Hu, M., Zheng, X., Chen, M., & Guo, S. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3147240 · Cited by 17

Deep Hybrid Models for Out-of-Distribution Detection

Cao, S., & Zhang, Z. M. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00469 · Cited by 20

Bayesian Diffusion Models for 3D Shape Reconstruction

Xu, H., Yu, L., Chen, Z., Zhang, X., Zhao, Y., Wang, Y., & Tu, Z. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01011 · Cited by 22

Preserving Modes and Messages via Diverse Particle Selection

Pacheco, J. L., Zuffi, S., Black, M. J., & Sudderth, E. B. (2014) · Cited by 17

Mathematical Modeling and Data Analysis of Nmr Experiments Using Hyperpolarized 13C Metabolites

Pagés, G., & Kuchel, P. W. (2013) · Magnetic Resonance Insights · doi:10.4137/mri.s11084 · Cited by 17

Bayesian negative binomial regression for differential expression with confounding factors

Dadaneh, S. Z., Zhou, M., & Qian, X. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty330 · Cited by 16

StreamGuard: A Bayesian Network Approach to Copyright Infringement Detection Problem in Large-scale Live Video Sharing Systems

Zhang, D., Song, L., Li, Q., Zhang, Y., & Wang, D. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622306 · Cited by 17

Fake News Detection Using Bayesian Inference

Najar, F., Zamzami, N., & Bouguila, N. (2019) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2019.00066 · Cited by 17

A Bayesian‐bandit adaptive design for N‐of‐1 clinical trials

Shrestha, S., & Jain, S. (2021) · Statistics in Medicine · doi:10.1002/sim.8873 · Cited by 17

Crowdsourcing System for Numerical Tasks based on Latent Topic Aware Worker Reliability

Shi, Z., Jiang, S., Zhang, L., Du, Y., & Li, X. (2021) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom42981.2021.9488748 · Cited by 17

Canonical Correlation Analysis on Data With Censoring and Error Information

Sun, J., & Keates, S. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2262949 · Cited by 17

Efficient reliability analysis based on Bayesian framework under input variable and metamodel uncertainties

An, D., & Choi, J. H. (2012) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-012-0776-6 · Cited by 17

Probabilistic analysis of communities and inner roles in networks: Bayesian generative models and approximate inference

Costa, G., & Ortale, R. (2013) · Social Network Analysis and Mining · doi:10.1007/s13278-013-0130-z · Cited by 17

Risk analysis for intellectual property litigation

Surdeanu, M., Nallapati, R. M., Gregory, G., Walker, J. H., & Manning, C. D. (2011) · International Conference on Artificial Intelligence and Law · doi:10.1145/2018358.2018375 · Cited by 17

Sometimes Average is Best: The Importance of Averaging for Prediction using MCMC Inference in Topic Modeling

Nguyen, V.-A., Boyd-Graber, J. L., & Resnik, P. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1182 · Cited by 17

A conditional random fields method for RNA sequence–structure relationship modeling and conformation sampling

Wang, Z., & Xu, J. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr232 · Cited by 17

Bayesian Analysis of Continuous Time Markov Chains with Application to Phylogenetic Modelling

Zhao, T., Wang, Z., Cumberworth, A., Gsponer, J., de Freitas, N., & Bouchard‐Côté, A. (2015) · Bayesian Analysis · doi:10.1214/15-ba982 · Cited by 17

Improved MCMC method for parameter estimation based on marginal probability density function

An, D., & Choi, J.-H. (2013) · Journal of Mechanical Science and Technology · doi:10.1007/s12206-013-0428-9 · Cited by 17

Ultra high energy neutrino–nucleon cross section from cosmic ray experiments and neutrino telescopes

Barger, V. D., Huber, P. J., & Marfatia, D. (2006) · Physics Letters B · doi:10.1016/j.physletb.2006.09.067 · Cited by 17

Minimax Mixing Time of the Metropolis-Adjusted Langevin Algorithm for Log-Concave SamplingPreprint

Wu, K., Schmidler, S. C., & Chen, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.13055 · Cited by 17

High performance latent dirichlet allocation for text mining

Liu, Z. (2013) · Brunel University Research Archive (BURA) (Brunel University London) · Cited by 17

Topic modeling and improvement of image representation for large-scale image retrieval

Tu, N. A., Dinh, D.-L., Rasel, M. K., & Lee, Y.-K. (2016) · Information Sciences · doi:10.1016/j.ins.2016.05.029 · Cited by 16

Event generation for probabilistic flood risk modelling: multi-site peak flow dependence model vs. weather-generator-based approach

Winter, B., Schneeberger, K., Förster, K., & Vorogushyn, S. (2020) · Natural hazards and earth system sciences · doi:10.5194/nhess-20-1689-2020 · Cited by 16

Conceptual Design of Extreme Sea-Level Early Warning Systems Based on Uncertainty Quantification and Engineering Optimization Methods

Denamiel, C., Huan, X., & Vilibić, I. (2021) · Frontiers in Marine Science · doi:10.3389/fmars.2021.650279 · Cited by 16

Near-optimal Quantum algorithms for multivariate mean estimation

Cornelissen, A., Hamoudi, Y., & Jerbi, S. (2022) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3519935.3520045 · Cited by 16

Bayesian neural network modeling of tree-ring temperature variability record from the Western Himalayas

Tiwari, R. K., & Maiti, S. (2011) · Nonlinear processes in geophysics · doi:10.5194/npg-18-515-2011 · Cited by 16

On UAV routing protocols for sparse sensor data exfiltration

Klein, D. J., Schweikl, J., Isaacs, J., & Hespanha, J. P. (2010) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2010.5531440 · Cited by 16

Subjective randomness and natural scene statistics

Hsu, A., Griffiths, T. L., & Schreiber, E. L. (2010) · Psychonomic Bulletin & Review · doi:10.3758/pbr.17.5.624 · Cited by 16

A Bayesian approach to joint tracking and identification of geometric shapes in video sequences

Minvielle, P., Arnaud Doucet, B., Marrs, A. D., & Maskell, S. R. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2009.05.002 · Cited by 16

Inferring Grammar-based Structure Models from 3D Microscopy Data

Schlecht, J., Barnard, K., Spriggs, E., & Pryor, B. M. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383031 · Cited by 16

A Numerical Comparison Between Quasi-Monte Carlo and Sparse Grid Stochastic Collocation Methods

Azevedo, J. S., & Oliveira, S. P. (2012) · Communications in Computational Physics · doi:10.4208/cicp.260111.230911a · Cited by 17

Decentralized Global Optimization Based on a Growth Transform Dynamical System Model

Chatterjee, O., & Chakrabartty, S. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2817367 · Cited by 16

Rao-Blackwellized sampling for batch and recursive Bayesian inference of Piecewise Affine models

Piga, D., Bemporad, A., & Benavoli, A. (2020) · Automatica · doi:10.1016/j.automatica.2020.109002 · Cited by 16

Invertible Neural Networks Versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescence

Andrle, A., Hegemann, N., Hagemann, P., Heidenreich, S., Soltwisch, V., & Steidl, G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-75549-2_42 · Cited by 16

A Bayesian Framework for Milling Stability Prediction and Reverse Parameter Identification

Cornelius, A., Karandikar, J. M., Gomez, M., & Schmitz, T. L. (2021) · Procedia Manufacturing · doi:10.1016/j.promfg.2021.06.073 · Cited by 16

Factorizing Historical User Actions for Next-Day Purchase Prediction

Liu, B., Zhang, H., Kong, L., & Niu, D. (2021) · ACM Transactions on the Web · doi:10.1145/3468227 · Cited by 16

Linear and non-linear bayesian regression methods for software fault prediction

Singh, R., & Rathore, S. S. (2022) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-021-01582-1 · Cited by 16

Speeding up constraint-based program repair using a search-based technique

Yi, J., & Ismayilzada, E. (2022) · Information and Software Technology · doi:10.1016/j.infsof.2022.106865 · Cited by 16

Information Encoding

Schuld, M., & Petruccione, F. (2018) · Quantum science and technology · doi:10.1007/978-3-319-96424-9_5 · Cited by 17

Kernel-phase detection limits

Ceau, A., Mary, D. L., Greenbaum, A. Z., Martinache, F., Sivaramakrishnan, A., Laugier, R., & N’Diaye, M. (2019) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201935247 · Cited by 16

Theoretical analysis and practical insights on importance sampling in Bayesian networks

Yuan, C., & Drużdżel, M. J. (2006) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2006.09.006 · Cited by 16

Latent Geospatial Semantics of Social Media

Sizov, S. (2012) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2337542.2337549 · Cited by 16

Variational Bayesian Generative Topographic Mapping

Olier, I., & Vellido, A. (2008) · Journal of Mathematical Modelling and Algorithms · doi:10.1007/s10852-008-9088-7 · Cited by 16

Learning to Select Supplier Portfolios for Service Supply Chain

Zhang, R., Li, J., Wu, S., & Meng, D. (2016) · PLoS ONE · doi:10.1371/journal.pone.0155672 · Cited by 18

Adjoint Hamiltonian Monte Carlo algorithm for the estimation of elastic modulus through the inversion of elastic wave propagation data

Koch, M. C., Fujisawa, K., & Murakami, A. (2019) · International Journal for Numerical Methods in Engineering · doi:10.1002/nme.6256 · Cited by 16

Machine Learning Methodology in Bioinformatics

Campbell, C. (2013) · Springer eBooks · doi:10.1007/978-3-642-30574-0_12 · Cited by 16

Conference Mining via Generalized Topic Modeling

Daud, A. Y., Li, J., Zhou, L., & Muhammad, F. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04180-8_33 · Cited by 17

Safe Control under UncertaintyPreprint

Sadigh, D., & Kapoor, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.07313 · Cited by 16

Does Hamiltonian Monte Carlo mix faster than a random walk on multimodal densities?Preprint

Mangoubi, O., Pillai, N. S., & Smith, A. D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.03230 · Cited by 16

An Adaptive Population Importance Sampler: Learning from the UncertanityPreprint

Martino, L., Elvira, V., Luengo, D., & Corander, J. (2014) · viXra · Cited by 16

An intelligent agent-based method for model updating of structures with multiple uncertain parameters via deep reinforcement learning

Bruno, G., Parisi, F., Ruggieri, S., & Uva, G. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.112832 · Cited by 17

Neural Graphical Models over Strings for Principal Parts Morphological Paradigm Completion

Cotterell, R., Sylak-Glassman, J., & Kirov, C. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-2120 · Cited by 14

A probabilistic model for fatigue crack growth prediction based on closed-form solution

Wang, T., Bahrami, Z., Renaud, G., Yang, C., Liao, M., & Liu, Z. (2022) · Structures · doi:10.1016/j.istruc.2022.08.066 · Cited by 15

An introduction to Markov Chain Monte Carlo

Esquivel, R. M., Gómez-Vargas, I., Vázquez, J. A., & García‐Salcedo, R. (2019) · doi:10.13140/rg.2.2.15462.43849 · Cited by 4

Introduction to Monte Carlo and MCMC Methods

Mira, A. (2016)

Markov Chain Monte Carlo Methods

Robert, C. P., & Richardson, S. T. (1998) · Lecture notes in statistics · doi:10.1007/978-1-4612-1716-9_1 · Cited by 27

Markov Chain Monte Carlo

Chakraborty, A. (2002) · Resonance · doi:10.1007/bf02836738 · Cited by 8

On the Markov Chain Monte Carlo (MCMC) method

Karandikar, R. L. (2006) · Sadhana · doi:10.1007/bf02719775 · Cited by 42