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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XSat: A Fast Floating-Point Satisfiability Solver

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Quantifying the uncertainty in model parameters using Gaussian process-based Markov chain Monte Carlo in cardiac electrophysiology

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Geometric ergodicity of the Bouncy Particle Sampler

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Approximate Bayesian computation (ABC) method for estimating parameters of the gamma process using noisy data

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Particle Filtering-Based Indoor Positioning System for Beacon Tag Tracking

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Bayesian neural networks for detecting epistasis in genetic association studies

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Data-driven MCMC for learning and inference in switching linear dynamic systems

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Understanding capacity fade in organic redox-flow batteries by combining spectroscopy with statistical inference techniques

Modak, S. V., Shen, W., Singh, S., Herrera, D., Oudeif, F., Goldsmith, B. R., Huan, X., & Kwabi, D. G. (2023) · Nature Communications · doi:10.1038/s41467-023-39257-z · Cited by 39

Fully adaptive Gaussian mixture Metropolis-Hastings algorithm

Luengo, D., & Martino, L. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6638846 · Cited by 38

Information-Maximization Clustering Based on Squared-Loss Mutual Information

Sugiyama, M., Niu, G., Yamada, M., Kimura, M., & Hachiya, H. (2013) · Neural Computation · doi:10.1162/neco_a_00534 · Cited by 38

Snow-melt flood frequency analysis by means of copula based 2D probability distributions for the Narew River in Poland

Ozga-Zieliński, B., Ciupak, M., Adamowski, J., Khalil, B., & Malard-Adam, J. J. (2016) · Journal of Hydrology Regional Studies · doi:10.1016/j.ejrh.2016.02.001 · Cited by 38

PT-LDA: A latent variable model to predict personality traits of social network users

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Orbits for 18 Visual Binaries and Two Double-line Spectroscopic Binaries Observed with HRCAM on the CTIO SOAR 4 m Telescope, Using a New Bayesian Orbit Code Based on Markov Chain Monte Carlo*

Mendez, R. A., Claveria, R. M., Orchard, M. E., & Silva, J. F. (2017) · The Astronomical Journal · doi:10.3847/1538-3881/aa8d6f · Cited by 38

Computing by modulating spontaneous cortical activity patterns as a mechanism of active visual processing

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Spatial Transition Learning on Road Networks with Deep Probabilistic Models

Li, X., Cong, G., & Cheng, Y. (2020) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde48307.2020.00037 · Cited by 38

Multilingual Dependency Parsing Using Global Features

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The mutual roles of temporal glimpsing and vocal characteristics in cocktail-party listening

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Probabilistic programming with programmable inference

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Annealed MAP

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Adsorption analysis of ciprofloxacin and delafloxacin onto the corn cob derived-biochar under different pyrolysis conditions

Gotore, O., Itayama, T., Dang, B., Nguyen, T.-D., Ramaraj, R., Nakagoe, O., Shuji, T., & Maseda, H. (2022) · Biomass Conversion and Biorefinery · doi:10.1007/s13399-022-03156-y · Cited by 39

Comparative analysis of five convolutional neural networks for landslide susceptibility assessment

Ge, Y., Liu, G., Tang, H., Zhao, B., & Xiong, C. (2023) · Bulletin of Engineering Geology and the Environment · doi:10.1007/s10064-023-03408-9 · Cited by 37

Parallel multivariate slice sampling

Tibbits, M., Haran, M., & Liechty, J. (2010) · Statistics and Computing · doi:10.1007/s11222-010-9178-z · Cited by 37

Efficient SRAM failure rate prediction via Gibbs sampling

Dong, C., & Li, X. (2011) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/2024724.2024769 · Cited by 37

From invariant checking to invariant inference using randomized search

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Bayesian reverse-engineering considered as a research strategy for cognitive science

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A one-pass sequential Monte Carlo method for Bayesian analysis of massive datasets

Balakrishnan, S., & Madigan, D. (2006) · Bayesian Analysis · doi:10.1214/06-ba112 · Cited by 37

A Study on Smoothing for Particle-Filtered 3D Human Body Tracking

Peursum, P., Venkatesh, S., & WEST, G. A. W. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-009-0205-5 · Cited by 38

Panlingual lexical translation via probabilistic inference

Mausam, M., Soderland, S. G., Etzioni, O., Weld, D. S., Reiter, K., Skinner, M., Sammer, M., & Bilmes, J. (2010) · Artificial Intelligence · doi:10.1016/j.artint.2010.04.020 · Cited by 36

Parameter optimization, sensitivity, and uncertainty analysis of an ecosystem model at a forest flux tower site in the United States

Wu, Y., Liu, S., Huang, Z., & Yan, W. (2014) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2013ms000298 · Cited by 36

Deep Learning for Epileptic Spike Detection

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All-Spin Bayesian Neural Networks

Yang, K., Malhotra, A., Lu, S., & Sengupta, A. (2020) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2020.2968223 · Cited by 37

A practical introduction to Bayesian estimation of causal effects: Parametric and nonparametric approaches

Oganisian, A., & Roy, J. (2020) · Statistics in Medicine · doi:10.1002/sim.8761 · Cited by 35

Extracting galactic binary signals from the first round of Mock LISA Data Challenges

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Efficient SRAM Failure Rate Prediction via Gibbs Sampling

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Probabilistic Inverse Theory

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Individual Differences in Motor Noise and Adaptation Rate Are Optimally Related

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Contextualize Your Listening: The Playlist as Recommendation Engine

Fields, B. (2011) · Goldsmiths (University of London) · Cited by 36

Bayesian Separation of Images Modeled With MRFs Using MCMC

Kayabol, K., Kuruoğlu, E. E., & Sankur, B. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2012905 · Cited by 35

Stochastic Optimization of Program Obfuscation

Liu, H., Sun, C. P., Su, Z., Jiang, Y., Gu, M., & Sun, J. (2017) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2017.28 · Cited by 35

On applying the Restricted Boltzmann Machine to active concept drift detection

Jaworski, M., Duda, P., & Rutkowski, L. (2017) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2017.8285409 · Cited by 34

Bayesian Co-Clustering Truth Discovery for Mobile Crowd Sensing Systems

Du, Y., Sun, Y.-E., Huang, H., Huang, L., Xu, H., Bao, Y., & Guo, H. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2896287 · Cited by 35

Monte Carlo Methods for Channel, Phase Noise, and Frequency Offset Estimation With Unknown Noise Variances in OFDM Systems

Septier, F., Delignon, Y., Rivenq, A., & Garnier, C. (2008) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2008.919629 · Cited by 35

Hierarchical Bayesian Language Models for Conversational Speech Recognition

Huang, S., & Renals, S. J. (2010) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2010.2040782 · Cited by 35

Adaptive Hamiltonian and Riemann Manifold Monte Carlo SamplersPreprint

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GFlowNet FoundationsPreprint

Bengio, Y., Lahlou, S., Deleu, T., Hu, J., Tiwari, M., & Bengio, E. (2021) · Journal of machine learning research · doi:10.48550/arxiv.2111.09266 · Cited by 404

The Recycling Gibbs sampler for efficient learning

Martino, L., Elvira, V., & Camps‐Valls, G. (2017) · Digital Signal Processing · doi:10.1016/j.dsp.2017.11.012 · Cited by 34

Bayesian Latent Class Analysis Tutorial

Li, Y., Lord‐Bessen, J., Shiyko, M. P., & Loeb, R. (2018) · Multivariate Behavioral Research · doi:10.1080/00273171.2018.1428892 · Cited by 40

Point Cloud Registration Based on MCMC-SA ICP Algorithm

Liu, H., Liu, T., Li, Y., Xi, M., Li, T., & Wang, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2919989 · Cited by 34

Empirical Assessment of Deep Gaussian Process Surrogate Models for Engineering Problems

Rajaram, D., Puranik, T. G., Renganathan, S. A., Sung, W., Fischer, O. J. P., Mavris, D. N., & Ramamurthy, A. (2020) · Journal of Aircraft · doi:10.2514/1.c036026 · Cited by 34

Approximate Bayesian Inference

Alquier, P. (2020) · Entropy · doi:10.3390/e22111272 · Cited by 37

ENSO‐GTC: ENSO Deep Learning Forecast Model With a Global Spatial‐Temporal Teleconnection Coupler

Mu, B., Qin, B., & Yuan, S. (2022) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2022ms003132 · Cited by 40

Tradeoffs among watershed model calibration targets for parameter estimation

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Epistemic Neural NetworksPreprint

Osband, I., Wen, Z., Asghari, S. M., Dwaracherla, V., Ibrahimi, M., Lu, X., & Roy, B. V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.08924 · Cited by 80

Truncation-free stochastic variational inference for Bayesian nonparametric models

Wang, C., & Blei, D. M. (2012) · Cited by 32

High-Resolution Radar Imaging in Complex Environments Based on Bayesian Learning With Mixture Models

Bai, X., Zhang, Y., & Zhou, F. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2863743 · Cited by 33

Predicting the broadly neutralizing antibody susceptibility of the HIV reservoir

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Multi Task Mutual Learning for Joint Sentiment Classification and Topic Detection

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Gibbs sampler and coordinate ascent variational inference: A set-theoretical review

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Sequential Monte-Carlo algorithms for Bayesian model calibration – A review and method comparison✰

Speich, M., Dormann, C. F., & Hartig, F. (2021) · Ecological Modelling · doi:10.1016/j.ecolmodel.2021.109608 · Cited by 33

Latent-Space Variational Bayes

Sung, J., Zoubin Ghahramani, A., & Bang, S.-Y. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.157 · Cited by 33

LMODEL: A Satellite Precipitation Methodology Using Cloud Development Modeling. Part I: Algorithm Construction and Calibration

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A low complexity user scheduling algorithm for uplink multiuser MIMO systems

Zhang, Y., Ji, C., Liu, Y., Malik, W. Q., O'Brien, D. C., & Edwards, D. (2008) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2008.070199 · Cited by 33

Reversible Jump MCMC

Fan, Y., & Sisson, S. A. (2011) · Chapman & Hall/CRC eBooks · doi:10.1201/b10905-4 · Cited by 33

An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics

Wang, L., Wang, S., & Bouchard‐Côté, A. (2019) · Systematic Biology · doi:10.1093/sysbio/syz028 · Cited by 33

Numerical synthesis of pontryagin optimal control minimizers using sampling-based methods

He, R., & González, H. (2017) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2017.8263747 · Cited by 32

Cyclone Trajectory and Intensity Prediction with Uncertainty Quantification Using Variational Recurrent Neural Networks

Kapoor, A., Negi, A., Marshall, L. A., & Chandra, R. (2022) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2023.105654 · Cited by 34

DressUp!

Yu, L.-F., Yeung, S.-K., Terzopoulos, D., & Chan, T. F.-C. (2012) · ACM Transactions on Graphics · doi:10.1145/2366145.2366153 · Cited by 32

Parameter and uncertainty estimation for process‐oriented population and distribution models: data, statistics and the niche

Marion, G., McInerny, G., Pagel, J., Catterall, S., Cook, A. R., Hartig, F., & O’Hara, R. B. (2012) · Journal of Biogeography · doi:10.1111/j.1365-2699.2012.02772.x · Cited by 32

Non-Parametric Estimation of Topic Hierarchies from Texts with Hierarchical Dirichlet Processes

Zavitsanos, E., Παλιούρας, Γ., & Vouros, G. A. (2011) · Journal of Machine Learning Research · doi:10.5555/1953048.2078193 · Cited by 32

Neural Topic Model with Reinforcement Learning

Gui, L., Leng, J. X., Pergola, G., Zhou, Y., Xu, R., & He, Y. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1350 · Cited by 32

Big geodata mining: Objective, connotations and research issues

Pei, T., Song, C., Guo, S., Shu, H., Liu, Y., Du, Y., Ma, T., & Zhou, C. (2020) · Journal of Geographical Sciences · doi:10.1007/s11442-020-1726-7 · Cited by 32

Fast Uncertainty Quantification for Deep Object Pose Estimation

Shi, G., Zhu, Y., Tremblay, J., Birchfield, S. T., Ramos, F. T., Anandkumar, A., & Zhu, Y. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561483 · Cited by 32

Neurosymbolic Models for Computer Graphics

Ritchie, D., Guerrero, P., Jones, R. K., Mitra, N. J., Schulz, A., Willis, K. D. D., & Wu, J. (2023) · Computer Graphics Forum · doi:10.1111/cgf.14775 · Cited by 43

Vision-based global localization for mobile robots with hybrid maps of objects and spatial layouts

Park, S., Kim, S., Park, M., & Park, S.-K. (2009) · Information Sciences · doi:10.1016/j.ins.2009.06.030 · Cited by 32

Application of loss functions in process economic risk assessment

Khan, F. I., Wang, H., & Yang, M. (2016) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2016.05.022 · Cited by 32

Mixture models for clustering and dimension reduction

Verbeek, J. J. (2004) · HAL · Cited by 32

Generalization Performance of Fisher Linear Discriminant Based on Markov Sampling

Zou, B., Li, L., Xu, Z., Luo, T., & Tang, Y. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2230406 · Cited by 31

On Learning Conditional Random Fields for Stereo

Pal, C., Weinman, J. J., Tran, L. C., & Scharstein, D. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-010-0385-z · Cited by 31

Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees

Zhang, Y., Lei, T., Barzilay, R., Jaakkola, T., & Globerson, A. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-1019 · Cited by 30

Bayesian Inference and Uncertainty Quantification for Medical Image Reconstruction with Poisson Data

Zhou, Q., Yu, T., Zhang, X., & Li, J. (2020) · SIAM Journal on Imaging Sciences · doi:10.1137/19m1248352 · Cited by 31

A spatial case-based reasoning method for regional landslide risk assessment

Zhao, Z., Chen, J., Xu, K., Xie, H., Gan, X., & Xu, H. (2021) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2021.102381 · Cited by 31

Learning with Limited Samples: Meta-Learning and Applications to Communication Systems

Chen, L., Jose, S. T., Nikoloska, I., Park, S., Chen, T., & Simeone, O. (2023) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000115 · Cited by 35

In situ monitoring and prediction of progressive joint wear using Bayesian statistics

An, D., Choi, J.-H., Schmitz, T. L., & Kim, N. H. (2011) · Wear · doi:10.1016/j.wear.2011.02.010 · Cited by 31

Safety assessment of trajectories for navigation in uncertain and dynamic environments

Althoff, D., Wollherr, D., & BUSS, M. J. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980115 · Cited by 31

Kinetics of the coagulation cascade including the contact activation system: sensitivity analysis and model reduction

Rojano, R. M., Mendez, S., Lucor, D., Ranc, A., Giansily‐Blaizot, M., Schved, J., & Nicoud, F. (2019) · Biomechanics and Modeling in Mechanobiology · doi:10.1007/s10237-019-01134-4 · Cited by 31

Deep Learning Methods for Small Molecule Drug Discovery: A Survey

Hu, W., Liu, Y., Chen, X., Chai, W., Chen, H., Wang, H., & Wang, G. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3251977 · Cited by 31

A Probabilistic Framework for Remaining Useful Life Prediction of Bearings

Wang, T., Liu, Z., & Mrad, N. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3029382 · Cited by 30

Demand-Side and Utility-Side Management Techniques for Increasing EV Charging Load

Shuvo, S. S., & Yılmaz, Y. (2023) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2023.3235903 · Cited by 30

Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm

Aghaei, V. T., Ağababaoğlu, A., Bawo, B., Naseradinmousavi, P., Yıldırım, S., Yeşilyurt, S., & Onat, A. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121108 · Cited by 30

A Generative Probabilistic Model for Multi-label Classification

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Approximation Methods for Efficient Learning of Bayesian Networks

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Rational Research model for ranking semantic entities

Wang, W., Barnaghi, P. M., & Andrzej Bargiela, A. (2011) · Information Sciences · doi:10.1016/j.ins.2011.02.028 · Cited by 29

Adaptive Learning in Bayesian Networks for Incident Duration Prediction

Demiroluk, S., & Özbay, K. M. A. (2014) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2460-09 · Cited by 29

Metamodel for Efficient Estimation of Capacity-Fade Uncertainty in Li-Ion Batteries for Electric Vehicles

Lee, J., Sung, W., & Choi, J.-H. (2015) · Energies · doi:10.3390/en8065538 · Cited by 30

Stochastic program optimization

Schkufza, E., Sharma, R., & Aiken, A. (2016) · Communications of the ACM · doi:10.1145/2863701 · Cited by 30

A Gibbs sampling algorithm for structural modal identification under seismic excitation

Li, B., Kiureghian, A. D., & Au, S. (2018) · Earthquake Engineering & Structural Dynamics · doi:10.1002/eqe.3094 · Cited by 29

A Bayesian perspective of statistical machine learning for big data

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Trace types and denotational semantics for sound programmable inference in probabilistic languages

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A Glucose-Insulin Mixture Model and Application to Short-Term Hypoglycemia Prediction in the Night Time

Wang, W., Wang, S., Wang, X., Liu, D., Geng, Y., & Wu, T. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.3015199 · Cited by 31

Exploiting Deep Learning for Secure Transmission in an Underlay Cognitive Radio Network

Zhang, M., Cumanan, K., Thiyagalingam, J., Tang, Y., Wang, W., Ding, Z., & Dobre, O. A. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3050104 · Cited by 29

An introduction to Markov Chain Monte Carlo

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Introduction to Monte Carlo and MCMC Methods

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Markov Chain Monte Carlo Methods

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On the Markov Chain Monte Carlo (MCMC) method

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