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

The Race to Improve Radar Imagery: An overview of recent progress in statistical sparsity-based techniques

Zhao, L., Wang, L., Yang, L., Zoubir, A. M., & Bi, G. (2016) · IEEE Signal Processing Magazine · doi:10.1109/msp.2016.2573847 · Cited by 78

Stochastic Analysis of the Normalized Subband Adaptive Filter Algorithm

Yin, W., & Mehr, A. S. (2010) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2010.2092130 · Cited by 77

International Tourism Dynamics in a Globalized World: A Social Network Analysis Approach

Chung, M. G., Herzberger, A., Frank, K. A., & Liu, J. (2019) · Journal of Travel Research · doi:10.1177/0047287519844834 · Cited by 77

Personalized Recommendations of Locally Interesting Venues to Tourists via Cross-Region Community Matching

Zhao, Y., Nie, L., Wang, X., & Chua, T. (2014) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2532439 · Cited by 77

Review of Monte Carlo Methods for the Prediction of Distributed Molecular and Morphological Polymer Properties

Meimaroglou, D., & Kiparissides, C. A. (2014) · Industrial & Engineering Chemistry Research · doi:10.1021/ie4033044 · Cited by 77

An Adaptive Population Importance Sampler: Learning From Uncertainty

Martino, L., Elvira, V., Luengo, D., & Corander, J. (2015) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2015.2440215 · Cited by 76

VIBNN

Cai, R., Ren, A., Liu, N., Ding, C., Wang, L., Qian, X., Pedram, M., & Wang, Y. (2018) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3173162.3173212 · Cited by 76

Model-data interaction in groundwater studies: Review of methods, applications and future directions

Rajabi, M. M., Ataie‐Ashtiani, B., & Simmons, C. T. (2018) · Journal of Hydrology · doi:10.1016/j.jhydrol.2018.09.053 · Cited by 79

Learning Travel Time Distributions with Deep Generative Model

Li, X., Cong, G., Sun, A., & Cheng, Y. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313418 · Cited by 74

DECODE: a new method for discovering clusters of different densities in spatial data

Pei, T., Jasra, A., Hand, D. J., Zhu, A., & Zhou, C. (2008) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-008-0120-3 · Cited by 74

Joint topic modeling for event summarization across news and social media streams

Gao, W. G., Li, P., & Darwish, K. M. (2012) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2396761.2398417 · Cited by 73

Pipeline condition monitoring towards digital twin system: A case study

Wang, T., Feng, K., Ling, J., Liao, M., Yang, C., Neubeck, R., & Liu, Z. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.02.006 · Cited by 73

Bayesian Exponential Family PCA

Mohamed, S., Zoubin Ghahramani, A., & Heller, K. (2008) · Cambridge University Engineering Department Publications Database · Cited by 73

Computational Foundations of Natural Intelligence

van Gerven, M. A. J. (2017) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2017.00112 · Cited by 76

Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries

Ziatdinov, M. A., Liu, Y., Morozovska, A. N., Eliseev, E. А., Zhang, X., Takeuchi, I., & Kalinin, S. V. (2022) · Advanced Materials · doi:10.1002/adma.202201345 · Cited by 84

Bayesian source term estimation of atmospheric releases in urban areas using LES approach

Xue, F., Kikumoto, H., Li, X., & Ooka, R. (2018) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2018.01.050 · Cited by 72

Compiling a massive, multilingual dictionary via probabilistic inference

Mausam, M., Soderland, S. G., Etzioni, O., Weld, D. S., Skinner, M., & Bilmes, J. (2009) · doi:10.3115/1687878.1687917 · Cited by 71

Joint Sparse Aperture ISAR Autofocusing and Scaling via Modified Newton Method-Based Variational Bayesian Inference

Zhang, S., Liu, Y., Li, X., & Bi, G. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2893505 · Cited by 71

Propulsive force measurements and flow behavior of undulatory swimmers at low Reynolds number

Sznitman, J., Shen, X., Sznitman, R., & Arratia, P. E. (2010) · Physics of Fluids · doi:10.1063/1.3529236 · Cited by 80

A theory of learning to infer.

Dasgupta, I., Schulz, E., Tenenbaum, J. B., & Gershman, S. J. (2020) · Psychological Review · doi:10.1037/rev0000178 · Cited by 70

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

Peters, G. W., Fan, Y., & Sisson, S. A. (2012) · Statistics and Computing · doi:10.1007/s11222-012-9315-y · Cited by 69

Bidirectional importance sampling for direct illumination

Burke, D., Ghosh, A., & Heidrich, W. (2005) · Eurographics Symposium on Rendering Techniques · doi:10.5555/2383654.2383675 · Cited by 69

Uncertainty-Aware Multidimensional Ensemble Data Visualization and Exploration

Chen, H., Zhang, S., Chen, W., Mei, H., Zhang, J., Mercer, A. E., Liang, R., & Qu, H. (2015) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2015.2410278 · Cited by 69

Emerging research topics detection with multiple machine learning models

Xu, S., Hao, L., An, X., Yang, G., & Wang, F. (2019) · Journal of Informetrics · doi:10.1016/j.joi.2019.100983 · Cited by 71

Structural damage detection based on variational Bayesian inference and delayed rejection adaptive Metropolis algorithm

Wang, X., Hou, R., Xia, Y., & Zhou, X. (2020) · Structural Health Monitoring · doi:10.1177/1475921720921256 · Cited by 68

Identifying biological concepts from a protein-related corpus with a probabilistic topic model

Zheng, B., McLean, D. C., & Lu, X. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-58 · Cited by 68

Bayesian inference of chemical kinetic models from proposed reactions

Galagali, N., & Marzouk, Y. (2014) · Chemical Engineering Science · doi:10.1016/j.ces.2014.10.030 · Cited by 69

Gaussian Process Regression for Sensor Networks Under Localization Uncertainty

Jadaliha, M., Xu, Y., Choi, J., Johnson, N. S., & Li, W. (2012) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2012.2223695 · Cited by 68

Multimodal Interactive Pattern Recognition and Applications

Toselli, A. H., Vidal, E., & Casacuberta, F. (2011) · Springer eBooks · doi:10.1007/978-0-85729-479-1 · Cited by 68

Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical Evaluations

Mahmud, M., & Ray, S. R. (2007) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 68

The Polarity Sensitivity of the Electrically Stimulated Human Auditory Nerve Measured at the Level of the Brainstem

Undurraga, J. A., Carlyon, R. P., Wouters, J. M., & Wieringen, A. V. (2013) · Journal of the Association for Research in Otolaryngology · doi:10.1007/s10162-013-0377-0 · Cited by 67

Time-domain Bayesian detection and estimation of noisy damped sinusoidal signals applied to NMR spectroscopy

Rubtsov, D. V., & Griffin, J. L. (2007) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2007.08.008 · Cited by 67

Modeling language and cognition with deep unsupervised learning: a tutorial overview

Zorzi, M., Testolin, A., & Stoianov, I. P. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00515 · Cited by 71

A Sequential Monte Carlo Method for Bayesian Analysis of Massive Datasets

Ridgeway, G. K., & Madigan, D. (2003) · Data Mining and Knowledge Discovery · doi:10.1023/a:1024084221803 · Cited by 67

Stochastic optimization of floating-point programs with tunable precision

Schkufza, E., Sharma, R., & Aiken, A. (2014) · ACM SIGPLAN Notices · doi:10.1145/2666356.2594302 · Cited by 66

Legal framework for small autonomous agricultural robots

Basu, S., Omotubora, A., Beeson, M., & Fox, C. (2018) · AI & Society · doi:10.1007/s00146-018-0846-4 · Cited by 66

Digital twins in process engineering: An overview on computational and numerical methods

Peterson, L., Gosea, I. V., Benner, P., & Sundmacher, K. (2024) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2024.108917 · Cited by 79

A novel contextual topic model for multi-document summarization

Yang, G., Wen, D., Kinshuk, K., Chen, N., & Sutinen, E. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.09.015 · Cited by 65

Group Importance Sampling for particle filtering and MCMC

Martino, L., Elvira, V., & Camps‐Valls, G. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2018.07.007 · Cited by 64

Probabilistic real-time deep-water natural gas hydrate dispersion modeling by using a novel hybrid deep learning approach

Shi, J., Li, J., Usmani, A. S., Zhu, Y. T., Chen, G., & Yang, D. (2020) · Energy · doi:10.1016/j.energy.2020.119572 · Cited by 64

A Survey of Uncertainty Quantification in Machine Learning for Space Weather Prediction

Siddique, T., Mahmud, M. S., Keesee, A. M., Ngwira, C. M., & Connor, H. K. (2022) · Geosciences · doi:10.3390/geosciences12010027 · Cited by 65

Efficient Probabilistic Classification Vector Machine With Incremental Basis Function Selection

Chen, H., Peter Tiňo, B., & Yao, X. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2275077 · Cited by 64

Tool life prediction using Bayesian updating. Part 2: Turning tool life using a Markov Chain Monte Carlo approach

Karandikar, J. M., Abbas, A. E., & Schmitz, T. L. (2013) · Precision Engineering · doi:10.1016/j.precisioneng.2013.06.007 · Cited by 64

A topic models based framework for detecting and forecasting emerging technologies

Xu, S., Hao, L., Yang, G., Lü, K., & An, X. (2020) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120366 · Cited by 63

The design and analysis of state-trace experiments.

Prince, M., Brown, S. D., & Heathcote, A. (2011) · Psychological Methods · doi:10.1037/a0025809 · Cited by 67

Adaptive Gaussian Process Approximation for Bayesian Inference with Expensive Likelihood Functions

Wang, H., & Li, J. (2018) · Neural Computation · doi:10.1162/neco_a_01127 · Cited by 63

A continuous modelling approach for design flood estimation on sub-daily time scale

Winter, B., Schneeberger, K., Dung, N. V., Huttenlau, M., Achleitner, S., Stötter, J., Merz, B., & Vorogushyn, S. (2019) · Hydrological Sciences Journal · doi:10.1080/02626667.2019.1593419 · Cited by 62

Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks

Sebastian, A., Pendurthi, R., Kozhakhmetov, A., Trainor, N., Robinson, J. A., Redwing, J. M., & Das, S. (2022) · Nature Communications · doi:10.1038/s41467-022-33699-7 · Cited by 62

Variational posterior distribution approximation in Bayesian super resolution reconstruction of multispectral images

Molina, R., Miguel Vega, B., Javier Mateos, A., & Katsaggelos, A. K. (2007) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2007.03.006 · Cited by 62

Adaptive Automation Assembly Systems in the Industry 4.0 Era: A Reference Framework and Full–Scale Prototype

Bortolini, M., Faccio, M., Galizia, F. G., Gamberi, M., & Pilati, F. (2021) · Applied Sciences · doi:10.3390/app11031256 · Cited by 61

Charles Bonnet Syndrome: Evidence for a Generative Model in the Cortex?

Reichert, D., Seriès, P., & Storkey, A. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003134 · Cited by 65

Alternative to Extended Block Sparse Bayesian Learning and Its Relation to Pattern-Coupled Sparse Bayesian Learning

Wang, L., Zhao, L., Rahardja, S., & Bi, G. (2018) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2816574 · Cited by 61

Unsupervised discovery of multilevel statistical video structures using hierarchical hidden Markov models

Xie, L., Chang, S., Divakaran, A., & Sun, H. (2003) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2003.1221240 · Cited by 62

Accelerating ABC methods using Gaussian processesPreprint

Wilkinson, R. D. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1401.1436 · Cited by 61

A Latent Topic Model for Complete Entity Resolution

Shu, L., Long, B., & Meng, W. (2009) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2009.29 · Cited by 59

Facilitating Bayesian analysis of combustion kinetic models with artificial neural network

Wang, J., Zhou, Z., Lin, K., Law, C. K., & Yang, B. (2019) · Combustion and Flame · doi:10.1016/j.combustflame.2019.11.035 · Cited by 60

Disentangling the effects of macroalgae on the abundance of temperate reef fishes

Pérez‐Matus, A., & Shima, J. (2010) · Journal of Experimental Marine Biology and Ecology · doi:10.1016/j.jembe.2010.03.013 · Cited by 60

Mocking the weak lensing universe: The LensTools Python computing package

Petri, A. (2016) · Astronomy and Computing · doi:10.1016/j.ascom.2016.06.001 · Cited by 49

Faster solutions of the inverse pairwise Ising problemPreprint

Broderick, T., Dudı́k, M., Tkacik, G., Schapire, R. E., & Bialek, W. (2007) · arXiv (Cornell University) · doi:10.48550/arxiv.0712.2437 · Cited by 80

Inverse Problems in Heat Transfer

Zabaras, N. J. (2000) · doi:10.1002/9780470172599.ch17 · Cited by 60

Parallel MCMC with Generalized Elliptical Slice SamplingPreprint

Nishihara, R., Murray, I., & Adams, R. P. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1210.7477 · Cited by 60

Generative learning facilitated discovery of high-entropy ceramic dielectrics for capacitive energy storage

Li, W., Shen, Z., Liu, R., Chen, X., Guo, M., Guo, J.-M., Hao, H., Shen, Y., Liu, H., Chen, L., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49170-8 · Cited by 73

An Efficient Evolutionary Algorithm for Chance-Constrained Bi-Objective Stochastic Optimization

Liu, B., Zhang, Q., Fernández, F. V., & Gielen, G. E. (2013) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2013.2244898 · Cited by 59

Identification of correlated damage parameters under noise and bias using Bayesian inference

An, D., Choi, J.-H., & Kim, N. H. (2011) · Structural Health Monitoring · doi:10.1177/1475921711424520 · Cited by 59

Large-scale offline signature recognition via deep neural networks and feature embedding

Çalık, N., Kurban, O. C., Yilmaz, A. R., Yıldırım, T., & Durak-Ata, L. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.03.027 · Cited by 60

Fast non-parametric simulation of 2D multi-layer cone penetration test (CPT) data without pre-stratification using Markov Chain Monte Carlo simulation

Zhao, T., Xu, L., & Wang, Y. (2020) · Engineering Geology · doi:10.1016/j.enggeo.2020.105670 · Cited by 59

Stochastic simulation under input uncertainty: A Review

Corlu, C. G., Akçay, A., & Xie, W. (2020) · Operations Research Perspectives · doi:10.1016/j.orp.2020.100162 · Cited by 60

Data-Driven Method for Predicting Remaining Useful Life of Bearing Based on Bayesian Theory

Gao, T., Li, Y., Huang, X., & Wang, C. (2020) · Sensors · doi:10.3390/s21010182 · Cited by 59

Computational system identification for Bayesian NARMAX modelling

Baldacchino, T., Anderson, S., & Kadirkamanathan, V. (2013) · Automatica · doi:10.1016/j.automatica.2013.05.023 · Cited by 58

Enhancing Text Categorization with Semantic-enriched Representation and Training Data Augmentation

Lu, X., Zheng, B., Velivelli, A., & Zhai, C. (2006) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m2051 · Cited by 59

Efficient cosmological parameter estimation with Hamiltonian Monte Carlo technique

Hajian, A. (2007) · Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields · doi:10.1103/physrevd.75.083525 · Cited by 58

Conditional Gaussian Systems for Multiscale Nonlinear Stochastic Systems: Prediction, State Estimation and Uncertainty Quantification

Chen, N., & Majda, A. J. (2018) · Entropy · doi:10.3390/e20070509 · Cited by 65

Super Resolution of Images and Video

Katsaggelos, A. K., Molina, R., & Javier Mateos, A. (2007) · Synthesis lectures on image, video, and multimedia processing · doi:10.2200/s00036ed1v01y200606ivm007 · Cited by 58

Stochastic superoptimization

Schkufza, E., Sharma, R., & Aiken, A. (2013) · ACM SIGARCH Computer Architecture News · doi:10.1145/2490301.2451150 · Cited by 57

Observations and stochastic modeling of soil moisture control on evapotranspiration in a Californian oak savanna

Chen, X., Rubin, Y. N., Ma, S., & Baldocchi, D. (2008) · Water Resources Research · doi:10.1029/2007wr006646 · Cited by 57

Cross-task crowdsourcing

Mo, K., Zhong, E., & Yang, Q. (2013) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2487575.2487593 · Cited by 57

On sequential data assimilation for scalar macroscopic traffic flow models

Blandin, S., Couque, A., Bayen, A. M., & Work, D. B. (2012) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2012.05.005 · Cited by 61

Temporal expert finding through generalized time topic modeling

Daud, A. Y., Li, J., Zhou, L., & Muhammad, F. (2010) · Knowledge-Based Systems · doi:10.1016/j.knosys.2010.04.008 · Cited by 58

Machine Learning for Speaker Recognition

Mak, M., & Chien, J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108552332 · Cited by 55

Detection of clustered microcalcifications using spatial point process modeling

Jing, H., Yang, Y., & Nishikawa, R. M. (2010) · Physics in Medicine and Biology · doi:10.1088/0031-9155/56/1/001 · Cited by 56

Using time topic modeling for semantics-based dynamic research interest finding

Daud, A. Y. (2011) · Knowledge-Based Systems · doi:10.1016/j.knosys.2011.07.015 · Cited by 57

Atmospheric inverse modeling with known physical bounds: an example from trace gas emissions

Miller, S. M., Michalak, A. M., & Levi, P. J. (2014) · Geoscientific model development · doi:10.5194/gmd-7-303-2014 · Cited by 59

Principled Selection of Hyperparameters in the Latent Dirichlet Allocation Model

George, C. P., & Doss, H. (2018) · Journal of Machine Learning Research · doi:10.5555/3122009.3242019 · Cited by 56

A Bayesian inference based adaptive lane change prediction model

Wang, J.-H., Zhang, Z.-Z., & Lu, G.-Q. (2021) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2021.103363 · Cited by 61

Active learning based sampling for high-dimensional nonlinear partial differential equations

Gao, W., & Wang, C. (2022) · Journal of Computational Physics · doi:10.1016/j.jcp.2022.111848 · Cited by 55

Multi-point source identification of sudden water pollution accidents in surface waters based on differential evolution and Metropolis–Hastings–Markov Chain Monte Carlo

Yang, H., Shao, D., Liu, B., Huang, J., & Ye, X. (2015) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-015-1191-5 · Cited by 55

Offline Evaluation of Ranking Policies with Click Models

Li, S., Abbasi-Yadkori, Y., Kveton, B., Muthukrishnan, S., Vinay, V., & Wen, Z. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220028 · Cited by 55

Training deep Boltzmann networks with sparse Ising machines

Niazi, S., Chowdhury, S., Aadit, N. A., Mohseni, M., Qin, Y., & Camsari, K. Y. (2024) · Nature Electronics · doi:10.1038/s41928-024-01182-4 · Cited by 74

Bayesian Nonparametric Reward Learning From Demonstration

Michini, B., Walsh, T. J., Agha–mohammadi, A., & How, J. P. (2015) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2405593 · Cited by 54

Confidence-Aware Matrix Factorization for Recommender Systems

Wang, C., Liu, Q., Wu, R., Chen, E., Liu, C., Huang, X., & Huang, Z. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11251 · Cited by 54

A Bayesian Network Approach for Condition Monitoring of High-Speed Railway Catenaries

Wang, H., Núñez, A. A., Liu, Z., Zhang, D., & Dollevoet, R. P. B. J. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2934346 · Cited by 54

A Markov model for short term wind speed prediction by integrating the wind acceleration information

Li, W., Jia, X., Li, X., Wang, Y., & Lee, J. (2020) · Renewable Energy · doi:10.1016/j.renene.2020.09.031 · Cited by 54

Adaptive MCMC with Bayesian Optimization

Mahendran, N., Wang, Z., Hamze, F., & de Freitas, N. (2012) · Cited by 54

Cell-Free DNA Analysis of Targeted Genomic Regions in Maternal Plasma for Non-Invasive Prenatal Testing of Trisomy 21, Trisomy 18, Trisomy 13, and Fetal Sex

Koumbaris, G., Kypri, E., Tsangaras, K., Achilleos, A., Mina, P., Neofytou, M. C., Velissariou, V., Christopoulou, G., Kallikas, I., González-Liñán, A., et al. (2016) · Clinical Chemistry · doi:10.1373/clinchem.2015.252502 · Cited by 53

Simulation Algorithms for Computational Systems Biology

Marchetti, L., Priami, C., & Thanh, V. H. (2017) · Texts in theoretical computer science · doi:10.1007/978-3-319-63113-4 · Cited by 53

Inverse problem instabilities in large-scale modeling of matter in extreme conditions

Kasim, M. F., Galligan, T. P., Topp-Mugglestone, J., Gregori, G., & Vinko, S. M. (2019) · Physics of Plasmas · doi:10.1063/1.5125979 · Cited by 53

Incremental knowledge base construction using DeepDive

De, C., Ratner, A., Ré, C., Shin, J., Wang, F., Wu, S., & Zhang, C. (2016) · The VLDB Journal · doi:10.1007/s00778-016-0437-2 · Cited by 57

Financial time series volatility analysis using Gaussian process state-space models

Han, J., & Zhang, X.-P. S. (2015) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2015.7418217 · Cited by 52

Using Bayesian-PBPK modeling for assessment of inter-individual variability and subgroup stratification

Krauß, M., Burghaus, R., Lippert, J., Niemi, M., Neuvonen, P. J., Schuppert, A., Willmann, S., Kuepfer, L., & Görlitz, L. (2013) · In Silico Pharmacology · doi:10.1186/2193-9616-1-6 · Cited by 52

Solving Factored MDPs with Hybrid State and Action Variables

Kveton, B., Hauskrecht, M., & Guestrin, C. (2006) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2085 · Cited by 52

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