An introduction to MCMC for machine learning.

Maximum Likelihood from Incomplete Data Via the EM Algorithm

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444

Optimization by Simulated Annealing

Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134

Equation of State Calculations by Fast Computing Machines

Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192

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

Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949

Monte Carlo sampling methods using Markov chains and their applications.

Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195

Untitled work

(1999) · Cited by 12,854

Markov Chain Monte Carlo in Practice

Gilks, W. R., Richardson, S. T., & Spiegelhalter, D. J. (1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835 · Cited by 7,927

Novel approach to nonlinear/non-Gaussian Bayesian state estimation

Gordon, N., Salmond, D. J., & Smith, A. F. M. (1993) · IEE Proceedings F Radar and Signal Processing · doi:10.1049/ip-f-2.1993.0015 · Cited by 7,711

Sequential Monte Carlo Methods in Practice

Arnaud Doucet, B. (2001) · Springer eBooks · doi:10.1007/978-1-4757-3437-9 · Cited by 7,516

Sampling-based approaches to calculating marginal densities

Gelfand, A. E., & Smith, A. F. M. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10476213 · Cited by 6,606

A Gallbladder Cancer Survival Prediction Model Based on Multimodal Fusion Analysis

Yin, Z., Chen, T., Shu, Y., Li, Q., Yuan, Z., Zhang, Y., Xu, X., & Liu, Y. (2022) · Digestive Diseases and Sciences · doi:10.1007/s10620-022-07782-4 · Cited by 15

Expert elicitation and data noise learning for material flow analysis using Bayesian inference

Dong, J., Liao, J., Huan, X., & Cooper, D. (2023) · Journal of Industrial Ecology · doi:10.1111/jiec.13399 · Cited by 16

Molecular Simulation Meets Machine Learning

Sadus, R. J. (2023) · Journal of Chemical & Engineering Data · doi:10.1021/acs.jced.3c00553 · Cited by 16

Construction and optimization of representative actual driving cycles based on the improved autoencoder

Zhao, Z., Sun, X., Wang, X., Wang, Y., Fu, J., & Liu, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-52865-z · Cited by 15

Flavor dependence of jet quenching in heavy-ion collisions from a Bayesian analysis

Zhang, S.-L., Wang, E., Xing, H., & Zhang, B.-W. (2024) · Physics Letters B · doi:10.1016/j.physletb.2024.138549 · Cited by 15

Extracting Agent-Based Models of Human Transportation Patterns

Beheshti, R., & Sukthankar, G. (2012) · International Conference on Social Informatics (SocialInformatics) · doi:10.1109/socialinformatics.2012.60 · Cited by 15

Adaptation and Change Detection With a Sequential Monte Carlo Scheme

Matsumoto, T., & Yosui, K. (2007) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2006.887431 · Cited by 15

Vision-based production of personalized video

Kosmopoulos, D., Doulamis, A. D., Makris, A., Doulamis, N. D., Chatzis, S., & Middleton, S. E. (2009) · Signal Processing Image Communication · doi:10.1016/j.image.2008.12.010 · Cited by 15

Time-of-flight PET image reconstruction using origin ensembles

Wülker, C., Sitek, A., & Prevrhal, S. (2015) · Physics in Medicine and Biology · doi:10.1088/0031-9155/60/5/1919 · Cited by 15

Monocular Human Motion Tracking with the DE-MC Particle Filter

Du, M., & Guan, L. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2006.1660315 · Cited by 15

Markov Chain Monte Carlo Based Energy Use Behaviors Prediction of Office Occupants

Yan, Q., Liu, X., Deng, X., Peng, W., & Zhang, G. (2020) · Algorithms · doi:10.3390/a13010021 · Cited by 15

Evasive behavior-based method for threat assessment in different scenarios: A novel framework for intelligent vehicle

Zhou, H., & Zhong, Z. (2020) · Accident Analysis & Prevention · doi:10.1016/j.aap.2020.105798 · Cited by 15

Estimating uncertainty: A Bayesian approach to modelling photosynthesis in C3 leaves

Xiao, Y., Sloan, J., Hepworth, C., Osborne, C. P., Fleming, A. J., Chen, X., & Zhu, X. (2021) · Plant Cell & Environment · doi:10.1111/pce.13995 · Cited by 15

Yes we care!-Certification for machine learning methods through the care label framework

Morik, K. J., Kotthaus, H., Fischer, R., Mücke, S., Jakobs, M., Piatkowski, N., Pauly, A., Heppe, L., & Heinrich, D. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.975029 · Cited by 15

A real-world application of Markov chain Monte Carlo method for Bayesian trajectory control of a robotic manipulator

Aghaei, V. T., Ağababaoğlu, A., Yıldırım, S., & Onat, A. (2021) · ISA Transactions · doi:10.1016/j.isatra.2021.06.010 · Cited by 15

Tracking Study on the Relapse and Aftercare Effect of Drug Patients Released From a Compulsory Isolated Detoxification Center

Liu, N., Lu, Z., & Xie, Y. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.699074 · Cited by 15

Differential Evolution Markov Chain Filter for Global Localization

Moreno, L. E., Martín, F., Muñoz, M. L., & Garrido, S. (2015) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-015-0245-8 · Cited by 15

Bayesian Nonparametric Inverse Reinforcement Learning for Switched Markov Decision Processes

Surana, A., & Srivastava, K. (2014) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2014.105 · Cited by 15

Improving the efficiency of Bayesian inverse reinforcement learning

Michini, B., & How, J. P. (2012) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2012.6225241 · Cited by 15

Mining topics on participations for community discovery

Zheng, G., Guo, J., Yang, L., Xu, S., Bao, S., Su, Z., Han, D., & Yu, Y. (2011) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2009916.2009977 · Cited by 15

Markov Chain Monte Carlo inference on graphical models using event-based processing on the SpiNNaker neuromorphic architecture

Mendat, D. R., Chin, S., Furber, S. B., & Andreou, A. G. (2015) · Conference on Information Sciences and Systems · doi:10.1109/ciss.2015.7086903 · Cited by 16

A Bayesian approach to support vector machines for the binary classification

Yu, J., Cheng, F., Xiong, H., Qu, W., & Chen, X. (2008) · Neurocomputing · doi:10.1016/j.neucom.2008.06.010 · Cited by 15

Exploiting Temporal Authors Interests via Temporal-Author-Topic Modeling

Daud, A. Y., Li, J., Zhou, L., & Muhammad, F. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03348-3_42 · Cited by 15

Nonparametric approach for uncertainty-based multidisciplinary design optimization considering limited data

Cho, S., Jang, J., Kim, S., Park, S., Lee, T. H., Lee, M., Choi, J.-S., Kim, H.-W., & Hong, S. (2016) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-016-1540-0 · Cited by 15

Bayesian methods for addressing long-standing problems in associative learning: The case of PREE

Blanco, F., & Morís, J. (2017) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470218.2017.1358292 · Cited by 16

Soft-Constrained Neural Networks for Nonparametric Density Estimation

Trentin, E. (2017) · Neural Processing Letters · doi:10.1007/s11063-017-9740-1 · Cited by 15

Hybrid Mixed-Membership Blockmodel for Inference on Realistic Network Interactions

Kao, E. K., Smith, S. T., & Airoldi, E. M. (2018) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2018.2823324 · Cited by 15

Bayesian inversion of inflow direction and speed in urban dispersion simulations

Li, X., & Xue, F. (2018) · Building and Environment · doi:10.1016/j.buildenv.2018.08.042 · Cited by 15

Bayesian Approach for Parameter Estimation in the Structural Analysis and Prognosis

Choi, J.-H., An, D., Gang, J.-H., Joo, J., & Kim, N. H. (2010) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2010.v2i1.1753 · Cited by 15

Extract interaction detection methods from the biological literature

Wang, H., Huang, M., & Zhu, X. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-s1-s55 · Cited by 15

An MCMC approach to solving hybrid factored MDPs

Kveton, B., & Hauskrecht, M. (2005) · Cited by 15

An objective function for STDP.Preprint

Bengio, Y., Mesnard, T., Fischer, A., Zhang, S., & Wu, Y. (2015) · arXiv (Cornell University) · Cited by 17

Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler DivergencePreprint

Jerfel, G., Wang, S., Fannjiang, C., Heller, K., Ma, Y.-A., & Jordan, M. I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.15980 · Cited by 15

Phase-dependent anisotropic Gaussian model for audio source separation

Magron, P., Badeau, R., & David, B. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952212 · Cited by 14

Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction

Zou, D., Xu, P., & Gu, Q. (2019) · neural information processing systems · Cited by 14

Degradation Modeling Based on Wiener Process Considering Multi-Source Heterogeneity

Xiao, M., Zhang, Y., Li, Y., & Wang, W. (2020) · IEEE Access · doi:10.1109/access.2020.3020723 · Cited by 14

Research on Vibration Reduction Control Based on Reinforcement Learning

Yuan, R., Yang, Y., Su, C., Hu, S., Zhang, H., & Cao, E. (2021) · Advances in Civil Engineering · doi:10.1155/2021/7619214 · Cited by 14

Bayesian analysis for estimating statistical parameter distributions of elasto-viscoplastic material models

Chakraborty, A., & Messner, M. C. (2021) · Probabilistic Engineering Mechanics · doi:10.1016/j.probengmech.2021.103153 · Cited by 14

Uncertainty in Engineering

Aslett, L. J. M., Coolen, F. P. A., & Bock, J. D. (2021) · SpringerBriefs in statistics · doi:10.1007/978-3-030-83640-5 · Cited by 14

Enhancing GUI Exploration Coverage of Android Apps with Deep Link-Integrated Monkey

Hu, H., Wang, H., Dong, R., Chen, X., & Chen, C. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3664810 · Cited by 14

Machine learning assisted calibration of PVT simulations for SiC crystal growth

Taucher, L., Ramadan, Z., Hammer, R., Obermüller, T., Auer, P., & Romaner, L. (2024) · CrystEngComm · doi:10.1039/d4ce00866a · Cited by 14

ALR-HT: A fast and efficient Lasso regression without hyperparameter tuning

Wang, Y., Zou, B., Xu, J., Xu, C., & Tang, Y. Y. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106885 · Cited by 16

Expectation-maximization algorithms for inference in Dirichlet processes mixture

Kimura, T., Tokuda, T., Nakada, Y., Nokajima, T., Matsumoto, T., & Arnaud Doucet, B. (2011) · Pattern Analysis and Applications · doi:10.1007/s10044-011-0256-4 · Cited by 14

Monte Carlo Bayesian inference on a statistical model of sub‐gridcolumn moisture variability using high‐resolution cloud observations. Part 1: Method

Norris, P. M., & da Silva, A. (2016) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.2843 · Cited by 14

Bayesian Analysis of an Excitonic Absorption Spectrum in a Cu2O Thin Film Sandwiched by Paired MgO Plates

Iwamitsu, K., Aihara, S., Okada, M., & Akai, I. (2016) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.85.094716 · Cited by 14

Herded gibbs sampling

Chen, Y., Bornn, L., de Freitas, N., Eskelin, M., Fang, J., & Welling, M. (2016) · UvA-DARE (University of Amsterdam) · doi:10.5555/2946645.2946655 · Cited by 14

Authenticity and credibility aware detection of adverse drug events from social media

Hoang, T., Liu, J., Pratt, N. L., Zheng, V. W., Chang, K. C., Roughead, E. E., & Li, J. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.10.003 · Cited by 14

Learning attribute grammars for movement primitive sequencing

Lioutikov, R., Maeda, G. J., Veiga, F. F., Kristian Kersting, B., & Peters, J. (2019) · The International Journal of Robotics Research · doi:10.1177/0278364919868279 · Cited by 14

Numerical-Based Approach for Updating Simulation Input in Real Time

Wu, L., & AbouRizk, S. (2020) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000948 · Cited by 14

Parallel exploration via negatively correlated search

Yang, P., Yang, Q., Tang, K., & Yao, X. (2021) · Frontiers of Computer Science · doi:10.1007/s11704-020-0431-0 · Cited by 14

Probabilistic Models for Text Mining

Sun, Y., Deng, H., & Han, J. (2012) · Springer eBooks · doi:10.1007/978-1-4614-3223-4_8 · Cited by 14

Bayesian statistical analysis of circadian oscillations in fibroblasts

Cohen, A. L., Leise, T., & Welsh, D. K. (2012) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2012.08.038 · Cited by 14

Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC

Pham, T. T., Chin, T.-J., Yu, J., & Suter, D. (2011) · Cited by 14

Bayesian Kernel Methods for Analysis of Functional Neuroimages

Lukic, A. S., Wernick, M. N., Tzikas, D. G., Chen, X., Likas, A. C., Galatsanos, N. P., Yang, Y., Zhao, F., & Strother, S. C. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.896934 · Cited by 14

A Generalized Topic Modeling Approach for Maven Search

Daud, A. Y., Li, J., Zhou, L., & Muhammad, F. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-00672-2_14 · Cited by 14

Nonparametric Bayesian Models for Unsupervised Scene Analysis and Reconstruction

Joho, D., Tipaldi, G. D., Engelhard, N., Stachniss, C., & Burgard, W. (2012) · Robotics: Science and Systems · doi:10.15607/rss.2012.viii.021 · Cited by 14

Generative Models for Sentiment Analysis and Opinion Mining

Wang, H., & Zhai, C. (2017) · Socio-affective computing · doi:10.1007/978-3-319-55394-8_6 · Cited by 14

Comparison of Deformation Models of Flexible Manipulator Joints for Use in DEMO

Li, M., Wu, H., Handroos, H., Skilton, R., Keep, J., & Loving, A. (2018) · IEEE Transactions on Plasma Science · doi:10.1109/tps.2018.2795084 · Cited by 14

MARKED POINT PROCESSES FOR THE AUTOMATIC DETECTION OF BOMB CRATERS IN AERIAL WARTIME IMAGES

Kruse, C., Rottensteiner, F., & Heipke, C. (2019) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-2-w13-51-2019 · Cited by 14

Dynamic and Static Topic Model for Analyzing Time-Series Document Collections

Hida, R., Takeishi, N., Yairi, T., & Hori, K. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-2082 · Cited by 14

Stochastic inversion method for dynamic constitutive model of rock materials based on improved DREAM

Zhai, Y., Zhao, R., Li, Y., Li, Y., Meng, F., & Wang, T. (2020) · International Journal of Impact Engineering · doi:10.1016/j.ijimpeng.2020.103739 · Cited by 14

Imitation and Reinforcement Learning for Motor Primitives with Perceptual Coupling

Kober, J., Mohler, B. J., & Peters, J. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-05181-4_10 · Cited by 14

Efficiently Learning Random Fields for Stereo Vision with Sparse Message Passing

Weinman, J. J., Tran, L., & Pal, C. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88682-2_47 · Cited by 14

A lightweight target-tracking scheme using wireless sensor network

Kuang, X., Feng, R., & Hui-he, S. (2008) · Measurement Science and Technology · doi:10.1088/0957-0233/19/2/025104 · Cited by 14

Multicanonical Monte-Carlo simulations of light propagation in biological media

Bilenca, A., Desjardins, A. E., Bouma, B. E., & Tearney, G. J. (2005) · Optics Express · doi:10.1364/opex.13.009822 · Cited by 14

The noninvasive reconstruction of 3D temperature field in a biological body with Monte Carlo method

Li, K., Dong, Y., Chen, C., & Zhang, S. (2008) · Neurocomputing · doi:10.1016/j.neucom.2008.03.016 · Cited by 14

Parallel gibbs sampling for hierarchical dirichlet processes via gamma processes equivalence

Cheng, D., & Liu, Y. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623708 · Cited by 14

Bayesian Models for Multilingual Word Alignment

Östling, R. (2015) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 14

LinkProbe: Probabilistic inference on large-scale social networks

Chen, H., Ku, W., Wang, H., Tang, L., & Sun, M.-T. (2013) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2013.6544833 · Cited by 13

Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models

Rostami, V., Mana, P. P., Grün, S., & Helias, M. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005762 · Cited by 14

Improvement in the accuracy of SPACE prediction for the unblocked FLECHT SEASET reflood tests by data assimilation

Tiep, N. H., Kim, K. D., & Heo, J. (2021) · Annals of Nuclear Energy · doi:10.1016/j.anucene.2021.108462 · Cited by 13

Systematic Bayesian posterior analysis guided by Kullback-Leibler divergence facilitates hypothesis formation

Huber, H. A., Georgia, S. K., & Finley, S. D. (2022) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2022.111341 · Cited by 13

Multiscale Time Series Decomposition for Structural Dynamic Properties: Long-Term Trend and Ambient Interference

Chu, X., Cui, W., Xu, S., Zhao, L., Guan, H., & Ge, Y. (2023) · Structural Control and Health Monitoring · doi:10.1155/2023/6485040 · Cited by 13

Interpretation of spatio-temporal variation of precipitation from spatially sparse measurements using Bayesian compressive sensing (BCS)

Li, P., & Wang, Y. (2023) · Georisk Assessment and Management of Risk for Engineered Systems and Geohazards · doi:10.1080/17499518.2023.2188464 · Cited by 14

Advancing probabilistic risk assessment by integrating human biomonitoring, new approach methods, and Bayesian modeling: A case study with the mycotoxin deoxynivalenol

Lu, E., Grimm, F. A., Rusyn, I., Saeger, S. D., Boevre, M. D., & Chiu, W. A. (2023) · Environment International · doi:10.1016/j.envint.2023.108326 · Cited by 13

AutoDDL: Automatic Distributed Deep Learning With Near-Optimal Bandwidth Cost

Chen, J., Li, S., Guo, R., Jinhui, Y., & Hoefler, T. (2024) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2024.3397800 · Cited by 16

Bayesian Policy Search for Multi-Agent Role Discovery

Wilson, A., Fern, A., & Tadepalli, P. V. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7679 · Cited by 13

Monocular Human Motion Tracking by Using DE-MC Particle Filter

Du, M., Nan, X., & Guan, L. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2263146 · Cited by 13

Automatic construction of anomaly detectors from graphical models

Ferragut, E. M., Darmon, D. M., Shue, C. A., & Kelley, S. (2011) · IEEE Symposium on Computational Intelligence in Cyber Security (CICS) · doi:10.1109/cicybs.2011.5949386 · Cited by 13

State space exploration using feedback constraint generation and Monte-Carlo sampling

Sankaranarayanan, S., Chang, R. M., Jiang, G., & Ivančić, F. (2007) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/1287624.1287670 · Cited by 13

Mutual benefit aware task assignment in a bipartite labor market

Liu, Z., & Chen, L. (2016) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2016.7498230 · Cited by 13

MCMC-Driven Adaptive Multiple Importance Sampling

Martino, L., Elvira, V., Luengo, D., & Corander, J. (2015) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-319-12454-4_8 · Cited by 13

Atmospheric inverse modeling via sparse reconstruction

Hase, N., Miller, S. M., Maaß, P., Notholt, J., Palm, M., & Warneke, T. (2017) · Geoscientific model development · doi:10.5194/gmd-10-3695-2017 · Cited by 13

Bayesian probabilistic multi-topic matrix factorization for rating prediction

Wang, K., Zhao, W. X., Hong-wei, P., & Wang, X. (2016) · International Joint Conference on Artificial Intelligence · Cited by 13

Probabilistic-Based Robotic Radiation Mapping Using Sparse Data

McDougall, R., Nokleby, S., & Waller, E. (2017) · Journal of Nuclear Engineering and Radiation Science · doi:10.1115/1.4038185 · Cited by 13

Bayesian constraint relaxation

Duan, L. L., Young, A. L., Nishimura, A., & Dunson, D. B. (2019) · Biometrika · doi:10.1093/biomet/asz069 · Cited by 13

Sampling-based causal inference in cue combination and its neural implementation

Yu, Z., Chen, F., Dong, J., & Dai, Q. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.10.045 · Cited by 12

Surprise About Sensory Event Timing Drives Cortical Transients in the Beta Frequency Band

Meindertsma, T., Kloosterman, N. A., Engel, A. K., Wagenmakers, E., & Donner, T. H. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.0307-18.2018 · Cited by 15

Life prediction for aircraft structure based on Bayesian inference: towards a digital twin ecosystem

Wang, T., Liu, Z., Liao, M., & Mrad, N. (2020) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2020.v12i1.1261 · Cited by 13

TAN-NTM: Topic Attention Networks for Neural Topic Modeling

Panwar, M., Shailabh, S., Aggarwal, M., & Krishnamurthy, B. (2021) · doi:10.18653/v1/2021.acl-long.299 · Cited by 18

Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning

Zaman, M. S., Dhamala, J., Bajracharya, P., Sapp, J. L., Horáček, B. M., Wu, K. C., Trayanova, N. A., & Wang, L. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.740306 · Cited by 13

A Bayesian Approach to Predict Football Matches with Changed Home Advantage in Spectator-Free Matches after the COVID-19 Break

Lee, J., Kim, J., Kim, J., & Lee, J. (2022) · Entropy · doi:10.3390/e24030366 · Cited by 13

Unsupervised Editing for Counterfactual Stories

Chen, J., Gan, C., Cheng, S., Zhou, H., Xiao, Y., & Li, L. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21290 · Cited by 14

Machine learning for predicting propensity-to-pay energy bills

Bashar, M. A., Nayak, R., Astin-Walmsley, K., & Kerina, H. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200176 · Cited by 13

Spatial shifting of COVID-19 clusters and disease association with environmental parameters in India: A time series analysis

Jana, A., Kundu, S., Shaw, S., Chakraborty, S., & Chattopadhyay, A. (2023) · Environmental Research · doi:10.1016/j.envres.2023.115288 · Cited by 13

Hybrid Auxiliary Field Quantum Monte Carlo for Molecular Systems

Chen, Y., Zhang, L., Weinan, E., & Car, R. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00038 · Cited by 13

Uncovering the Location of Twitter Users

Rodrigues, É. C., Assunção, R., Pappa, G. L., Miranda, R., & Meira, W. (2013) · Brazilian Conference on Intelligent Systems · doi:10.1109/bracis.2013.47 · Cited by 13

Hyperpolarized [1,13C]pyruvate in lysed human erythrocytes: effects of co‐substrate supply on reaction time courses

Pagés, G., Tan, Y. L., & Kuchel, P. W. (2014) · NMR in Biomedicine · doi:10.1002/nbm.3176 · Cited by 13

Estimating Missing Data and Determining the Confidence of the Estimate Data

Mistry, J., Nelwamondo, F. V., & Marwala, T. (2008) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2008.71 · Cited by 13

Parameter Estimation for an Inverse Nonlinear Stochastic Problem: Reactivity Ratio Studies in Copolymerization

Du, Y., Budman, H. M., & Duever, T. A. (2017) · Macromolecular Theory and Simulations · doi:10.1002/mats.201600095 · Cited by 13

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