2,048
Views
88
CrossRef citations to date
0
Altmetric
`Big Data and Information Theory' in celebrating the 95th birthday of Professor Lotfi A. Zadeh

Bayes and big data: the consensus Monte Carlo algorithm

, , , , &
Pages 78-88 | Received 31 Oct 2013, Accepted 01 Dec 2015, Published online: 16 Feb 2016

Keep up to date with the latest research on this topic with citation updates for this article.

Read on this site (19)

Qurat-Ul-Ain Mahmood, Riaz Ahmed & Simon P. Philbin. (2023) The moderating effect of big data analytics on green human resource management and organizational performance. International Journal of Management Science and Engineering Management 18:3, pages 177-189.
Read now
Jiayuan Zhou, Kshitij Khare & Sanvesh Srivastava. (2023) Asynchronous and Distributed Data Augmentation for Massive Data Settings. Journal of Computational and Graphical Statistics 32:3, pages 895-907.
Read now
Hang Qian. (2022) Bayesian Inference in Common Microeconometric Models With Massive Datasets by Double Marginalized Subsampling. Journal of Business & Economic Statistics 40:4, pages 1484-1497.
Read now
Sehwan Kim, Qifan Song & Faming Liang. (2022) Stochastic gradient Langevin dynamics with adaptive drifts. Journal of Statistical Computation and Simulation 92:2, pages 318-336.
Read now
Tung-Yu Wu, Y. X. Rachel Wang & Wing H. Wong. (2022) Mini-Batch Metropolis–Hastings With Reversible SGLD Proposal. Journal of the American Statistical Association 117:537, pages 386-394.
Read now
Randy C. S. Lai, Jan Hannig & Thomas C. M. Lee. (2021) Method G: Uncertainty Quantification for Distributed Data Problems Using Generalized Fiducial Inference. Journal of Computational and Graphical Statistics 30:4, pages 934-945.
Read now
Sanvesh Srivastava & Yixiang Xu. (2021) Distributed Bayesian Inference in Linear Mixed-Effects Models. Journal of Computational and Graphical Statistics 30:3, pages 594-611.
Read now
Maxime Vono, Nicolas Dobigeon & Pierre Chainais. (2021) Asymptotically Exact Data Augmentation: Models, Properties, and Algorithms. Journal of Computational and Graphical Statistics 30:2, pages 335-348.
Read now
Lewis J. Rendell, Adam M. Johansen, Anthony Lee & Nick Whiteley. (2021) Global Consensus Monte Carlo. Journal of Computational and Graphical Statistics 30:2, pages 249-259.
Read now
Mevin B. Hooten, Devin S. Johnson & Brian M. Brost. (2021) Making Recursive Bayesian Inference Accessible. The American Statistician 75:2, pages 185-194.
Read now
Christopher Nemeth & Paul Fearnhead. (2021) Stochastic Gradient Markov Chain Monte Carlo. Journal of the American Statistical Association 116:533, pages 433-450.
Read now
Yang Ni, Yuan Ji & Peter Müller. (2020) Consensus Monte Carlo for Random Subsets Using Shared Anchors. Journal of Computational and Graphical Statistics 29:4, pages 703-714.
Read now
Yang Ni, Peter Müller & Yuan Ji. (2020) Bayesian Double Feature Allocation for Phenotyping With Electronic Health Records. Journal of the American Statistical Association 115:532, pages 1620-1634.
Read now
Yang Ni, Peter Müller, Maurice Diesendruck, Sinead Williamson, Yitan Zhu & Yuan Ji. (2020) Scalable Bayesian Nonparametric Clustering and Classification. Journal of Computational and Graphical Statistics 29:1, pages 53-65.
Read now
Zheng Wei & Erin M. Conlon. (2019) Parallel Markov chain Monte Carlo for Bayesian hierarchical models with big data, in two stages. Journal of Applied Statistics 46:11, pages 1917-1936.
Read now
Jinyoung Yang, Evgeny Levi, Radu V. Craiu & Jeffrey S. Rosenthal. (2019) Adaptive Component-Wise Multiple-Try Metropolis Sampling. Journal of Computational and Graphical Statistics 28:2, pages 276-289.
Read now
Michael I. Jordan, Jason D. Lee & Yun Yang. (2019) Communication-Efficient Distributed Statistical Inference. Journal of the American Statistical Association 114:526, pages 668-681.
Read now
Rajarshi Guhaniyogi & Sudipto Banerjee. (2018) Meta-Kriging: Scalable Bayesian Modeling and Inference for Massive Spatial Datasets. Technometrics 60:4, pages 430-444.
Read now
Jarod Y. L. Lee, James J. Brown & Louise M. Ryan. (2017) Sufficiency Revisited: Rethinking Statistical Algorithms in the Big Data Era. The American Statistician 71:3, pages 202-208.
Read now

Articles from other publishers (69)

Christian Soize. (2022) Probabilistic learning constrained by realizations using a weak formulation of Fourier transform of probability measures. Computational Statistics 38:4, pages 1879-1925.
Crossref
Callum Vyner, Christopher Nemeth & Chris Sherlock. (2023) SwISS: A scalable Markov chain Monte Carlo divide‐and‐conquer strategy. Stat 12:1.
Crossref
Alexander Buchholz, Daniel Ahfock & Sylvia Richardson. (2023) Distributed Computation for Marginal Likelihood based Model Choice. Bayesian Analysis 18:2.
Crossref
O. Ezvan, C. Soize, C. Desceliers & R. Ghanem. (2023) Updating an uncertain and expensive computational model in structural dynamics based on one single target FRF using a probabilistic learning tool. Computational Mechanics 71:6, pages 1161-1177.
Crossref
Elena Pesce, Francesco Porro & Eva Riccomagno. (2022) Large datasets, bias and model‐oriented optimal design of experiments. Quality and Reliability Engineering International 39:2, pages 532-545.
Crossref
Hongsheng Dai, Murray Pollock & Gareth O Roberts. (2023) Bayesian fusion: scalable unification of distributed statistical analyses. Journal of the Royal Statistical Society Series B: Statistical Methodology 85:1, pages 84-107.
Crossref
Chunlei Wang & Sanvesh Srivastava. (2023) Divide-and-conquer Bayesian inference in hidden Markov models. Electronic Journal of Statistics 17:1.
Crossref
Fuyi Huang, Sheng Zhang, Jiashu Zhang, Hongyang Chen & Ali H. Sayed. (2023) Diffusion Bayesian Decorrelation Algorithms Over Networks. IEEE Transactions on Signal Processing 71, pages 571-586.
Crossref
Hari Hara Suthan Chittoor & Osvaldo Simeone. (2022) Robust Distributed Bayesian Learning with Stragglers via Consensus Monte Carlo. Robust Distributed Bayesian Learning with Stragglers via Consensus Monte Carlo.
Nariankadu D. Shyamalkumar & Sanvesh Srivastava. (2022) An algorithm for distributed Bayesian inference. Stat 11:1.
Crossref
Radu V. Craiu, Paul Gustafson & Jeffrey S. Rosenthal. (2022) Reflections on Bayesian inference and Markov chain Monte Carlo. Canadian Journal of Statistics 50:4, pages 1213-1227.
Crossref
Jason S. Byers & Jeff Gill. (2022) Applied Geospatial Bayesian Modeling in the Big Data Era: Challenges and Solutions. Mathematics 10:21, pages 4116.
Crossref
Fernando Llorente, Ernesto Curbelo, Luca Martino, Pablo Olmos & David Delgado. (2022) Safe importance sampling based on partial posteriors and neural variational approximations. Safe importance sampling based on partial posteriors and neural variational approximations.
Stephen Coleman, Paul D. W. Kirk & Chris Wallace. (2022) Consensus clustering for Bayesian mixture models. BMC Bioinformatics 23:1.
Crossref
Kaixuan Luo, Jianling Zhong, Alexias Safi, Linda K. Hong, Alok K. Tewari, Lingyun Song, Timothy E. Reddy, Li Ma, Gregory E. Crawford & Alexander J. Hartemink. (2022) Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data. Genome Research 32:6, pages 1183-1198.
Crossref
Claudio J. Bordin, Caio G. de Figueredo & Marcelo G. S. Bruno. (2022) Distributed Particle Filters for State Tracking on the Stiefel Manifold Using Tangent Space Statistics. Distributed Particle Filters for State Tracking on the Stiefel Manifold Using Tangent Space Statistics.
Hai Li, Guoqiang Xue & Wen Chen. (2022) Bayesian subsampling of time-domain electromagnetic data using kernel density product. GEOPHYSICS 87:2, pages E79-E90.
Crossref
Dongzhu Liu & Osvaldo Simeone. (2022) Channel-Driven Monte Carlo Sampling for Bayesian Distributed Learning in Wireless Data Centers. IEEE Journal on Selected Areas in Communications 40:2, pages 562-577.
Crossref
Evgeny Levi & Radu V. Craiu. (2022) Finding our Way in the Dark: Approximate MCMC for Approximate Bayesian Methods. Bayesian Analysis 17:1.
Crossref
Rahif Kassab & Osvaldo Simeone. (2022) Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent. IEEE Transactions on Signal Processing 70, pages 2180-2192.
Crossref
Peng Zhao & Guanyu Hu. (2021) Approximate Bayesian Estimation with Subsampled Logistic Regression in Big Data Settings. Approximate Bayesian Estimation with Subsampled Logistic Regression in Big Data Settings.
Hai Li, Guoqiang Xue & Linbo Zhang. (2021) Accelerated Bayesian Inversion of Transient Electromagnetic Data Using MCMC Subposteriors. IEEE Transactions on Geoscience and Remote Sensing 59:12, pages 10000-10010.
Crossref
Edgar Santos‐Fernandez & Kerrie Mengersen. (2021) Understanding the reliability of citizen science observational data using item response models. Methods in Ecology and Evolution 12:8, pages 1533-1548.
Crossref
Guanyu Hu & Haiying Wang. (2020) Most Likely Optimal Subsampled Markov Chain Monte Carlo. Journal of Systems Science and Complexity 34:3, pages 1121-1134.
Crossref
Luca Martino & Víctor Elvira. (2021) Compressed Monte Carlo with application in particle filtering. Information Sciences 553, pages 331-352.
Crossref
Madhavi Arun Vaidya & Meghana Sanjeeva. 2021. Handbook of Research on Modern Educational Technologies, Applications, and Management. Handbook of Research on Modern Educational Technologies, Applications, and Management 48 67 .
Jose Cadena, Priyadip Ray, Hao Chen, Braden Soper, Deepak Rajan, Anton Yen & Ryan Goldhahn. (2021) Stochastic Gradient-Based Distributed Bayesian Estimation in Cooperative Sensor Networks. IEEE Transactions on Signal Processing 69, pages 1713-1724.
Crossref
Weihao Song, Zidong Wang, Jianan Wang, Fuad E. Alsaadi & Jiayuan Shan. (2021) Particle Filtering for Nonlinear/Non-Gaussian Systems With Energy Harvesting Sensors Subject to Randomly Occurring Sensor Saturations. IEEE Transactions on Signal Processing 69, pages 15-27.
Crossref
Yang Ni, David Jones & Zeya Wang. (2020) Consensus Variational and Monte Carlo Algorithms for Bayesian Nonparametric Clustering. Consensus Variational and Monte Carlo Algorithms for Bayesian Nonparametric Clustering.
Federico (Rico) Bumbaca, Sanjog Misra & Peter E. Rossi. (2020) Scalable Target Marketing: Distributed Markov Chain Monte Carlo for Bayesian Hierarchical Models. Journal of Marketing Research, pages 002224372095241.
Crossref
Teng Xu, Sebastian Reuschen, Wolfgang Nowak & Harrie‐Jan Hendricks Franssen. (2020) Preconditioned Crank‐Nicolson Markov Chain Monte Carlo Coupled With Parallel Tempering: An Efficient Method for Bayesian Inversion of Multi‐Gaussian Log‐Hydraulic Conductivity Fields. Water Resources Research 56:8.
Crossref
Hongsheng Dai. 2020. Bayesian Inference on Complicated Data. Bayesian Inference on Complicated Data.
Alessandro Emanuele, Francesco Gasparotto, Giacomo Guerra & Mattia Zorzi. (2020) Robust Distributed Kalman Filtering: On the Choice of the Local Tolerance. Sensors 20:11, pages 3244.
Crossref
Andrés F. Barrientos & Víctor Peña. (2020) Bayesian Bootstraps for Massive Data. Bayesian Analysis 15:2.
Crossref
Iftikhar Ahmad, Ahsan Ayub, Manabu Kano & Izzat Iqbal Cheema. (2020) Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data. Processes 8:2, pages 243.
Crossref
Xiaohong Liu, Ruiqing Sun, Shiyun Wang & Yenchun Jim Wu. (2019) The research landscape of big data: a bibliometric analysis. Library Hi Tech 38:2, pages 367-384.
Crossref
Wenjing Wang & Xi Chen. (2019) Distributed Variational Inference-Based Heteroscedastic Gaussian Process Metamodeling. Distributed Variational Inference-Based Heteroscedastic Gaussian Process Metamodeling.
Xiangju Qin, Paul Blomstedt, Eemeli Leppäaho, Pekka Parviainen & Samuel Kaski. (2019) Distributed Bayesian matrix factorization with limited communication. Machine Learning 108:10, pages 1805-1830.
Crossref
Alexey Miroshnikov & Evgeny Savelev. (2018) Asymptotic properties of parallel Bayesian kernel density estimators. Annals of the Institute of Statistical Mathematics 71:4, pages 771-810.
Crossref
Joris Bierkens, Paul Fearnhead & Gareth Roberts. (2019) The Zig-Zag process and super-efficient sampling for Bayesian analysis of big data. The Annals of Statistics 47:3.
Crossref
Xinxin Wang, Zeshui Xu & Xunjie Gou. (2019) Consensus-Based Track Association with Multistatic Sensors under a Nested Probabilistic-Numerical Linguistic Environment. Sensors 19:6, pages 1381.
Crossref
Robert J. B. Goudie, Anne M. Presanis, David Lunn, Daniela De Angelis & Lorenz Wernisch. (2019) Joining and Splitting Models with Markov Melding. Bayesian Analysis 14:1.
Crossref
M. Antónia Amaral Turkman, Carlos Daniel Paulino & Peter Müller. 2019. Computational Bayesian Statistics. Computational Bayesian Statistics.
Mahdi Fahmideh & Ghassan Beydoun. (2019) Big data analytics architecture design—An application in manufacturing systems. Computers & Industrial Engineering 128, pages 948-963.
Crossref
Stanislav Minsker. (2019) Distributed statistical estimation and rates of convergence in normal approximation. Electronic Journal of Statistics 13:2.
Crossref
Felippe Cronemberger & J. Ramon Gil-Garcia. 2019. Setting Foundations for the Creation of Public Value in Smart Cities. Setting Foundations for the Creation of Public Value in Smart Cities 247 267 .
Youngdeok Hwang, Siyuan Lu & Jae-Kwang Kim. (2018) Bottom-up estimation and top-down prediction: Solar energy prediction combining information from multiple sources. The Annals of Applied Statistics 12:4.
Crossref
Refik Soyer. (2018) Kalman filtering and sequential Bayesian analysis. WIREs Computational Statistics 10:5.
Crossref
Christian P. Robert, Víctor Elvira, Nick Tawn & Changye Wu. (2018) Accelerating MCMC algorithms. WIREs Computational Statistics 10:5.
Crossref
Paul Fearnhead, Joris Bierkens, Murray Pollock & Gareth O. Roberts. (2018) Piecewise Deterministic Markov Processes for Continuous-Time Monte Carlo. Statistical Science 33:3.
Crossref
Dean Teffer & Joydeep Ghosh. (2018) Non-parametric Discovery of Topics and Communities in Distributed and Streaming Environments. Non-parametric Discovery of Topics and Communities in Distributed and Streaming Environments.
David Luengo, Luca Martino, Víctor Elvira & Mónica Bugallo. (2018) Efficient linear fusion of partial estimators. Digital Signal Processing 78, pages 265-283.
Crossref
Christopher Nemeth & Chris Sherlock. (2018) Merging MCMC Subposteriors through Gaussian-Process Approximations. Bayesian Analysis 13:2.
Crossref
Daiane Aparecida Zuanetti, Peter MüllerYitan ZhuShengjie YangYuan Ji. (2018) Clustering distributions with the marginalized nested Dirichlet process. Biometrics 74:2, pages 584-594.
Crossref
Petro Kosobutskyy, Mykhaylo Lobur, Serhiy Shcherbovskykh & Tetyana Stefanovych. (2018) Overlap estimation of two normally distributed systems based on Monte-Carlo simulation. Overlap estimation of two normally distributed systems based on Monte-Carlo simulation.
Daniel Heestermans Svendsen, Luca Martino, Manuel Campos-Taberner, Francisco Javier Garcia-Haro & Gustau Camps-Valls. (2018) Joint Gaussian Processes for Biophysical Parameter Retrieval. IEEE Transactions on Geoscience and Remote Sensing 56:3, pages 1718-1727.
Crossref
Reihaneh Entezari, Radu V. Craiu & Jeffrey S. Rosenthal. (2018) Likelihood inflating sampling algorithm. Canadian Journal of Statistics 46:1, pages 147-175.
Crossref
Ernesto Damiani, Ryszard Kowalczyk & Gerard Parr. (2017) Extending the Outreach. ACM Transactions on Internet Technology 18:1, pages 1-7.
Crossref
Ali Bakdur, Fumito Masui & Michal Ptaszynski. (2018) Big data analytics - towards the enrichment of content tourism for revitalization of Japanese rural area. MATEC Web of Conferences 169, pages 01008.
Crossref
Steven L. Scott. (2017) Rejoinder. Brazilian Journal of Probability and Statistics 31:4.
Crossref
Steven L. Scott. (2017) Comparing consensus Monte Carlo strategies for distributed Bayesian computation. Brazilian Journal of Probability and Statistics 31:4.
Crossref
Balazs Nemeth, Tom Haber, Jori Liesenborgs & Wim Lamotte. (2017) Distributed Affine-Invariant MCMC Sampler. Distributed Affine-Invariant MCMC Sampler.
Dongmei Lin. (2017) Research on the Information Construction of Accounting Audit Based on the Big Data of Computer. International Journal of Information Technology and Web Engineering 12:3, pages 74-82.
Crossref
Nicolas Chopin & James Ridgway. (2017) Leave Pima Indians Alone: Binary Regression as a Benchmark for Bayesian Computation. Statistical Science 32:1.
Crossref
Zheng Wei, Xiaojing Wang & Erin Marie Conlon. (2017) Parallel Markov chain Monte Carlo for Bayesian dynamic item response models in educational testing. Stat 6:1, pages 420-433.
Crossref
Matthew T. Pratola. (2016) Rejoinder. Bayesian Analysis 11:3.
Crossref
Riaz Ahmed. (2022) The moderating effect of big data analytics on green human resource management and organizational performance. SSRN Electronic Journal.
Crossref
Federico Bumbaca, Sanjog Misra & Peter E. Rossi. (2017) Distributed Markov Chain Monte Carlo for Bayesian Hierarchical Models. SSRN Electronic Journal.
Crossref
Matias Quiroz, Mattias Villani & Robert Kohn. (2015) Scalable MCMC for Large Data Problems Using Data Subsampling and the Difference Estimator. SSRN Electronic Journal.
Crossref

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.