Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition

Flexible paleoclimate age-depth models using an autoregressive gamma process

Blaauw, M., & Christen, J. A. (2011) · Bayesian Analysis · doi:10.1214/ba/1339616472 · Cited by 4,621

Detection of B -Mode Polarization at Degree Angular Scales by BICEP2

Ade, P. A. R., Aikin, R. W., Barkats, D., Benton, S. J., Bischoff, C. A., Bock, J. J., Brevik, J. A., Buder, I., Bullock, E., Dowell, C. D., et al. (2014) · Physical Review Letters · doi:10.1103/physrevlett.112.241101 · Cited by 1,572

Bayesian statistics and modelling

van de Schoot, R., Depaoli, S. A., King, R., Kramer, B. M. R., Märtens, K., Tadesse, M. G., Vannucci, M., Gelman, A., Veen, D., Willemsen, J. E., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-020-00001-2 · Cited by 1,338

Metaheuristic research: a comprehensive survey

Hussain, K., Salleh, M. N. M., Cheng, S., & Shi, Y. (2018) · Artificial Intelligence Review · doi:10.1007/s10462-017-9605-z · Cited by 913

A simple introduction to Markov Chain Monte–Carlo sampling

van Ravenzwaaij, D., Cassey, P., & Brown, S. D. (2016) · Psychonomic Bulletin & Review · doi:10.3758/s13423-016-1015-8 · Cited by 622

Reduction in mobility and COVID-19 transmission

Nouvellet, P., Bhatia, S., Cori, A., Ainslie, K. E. C., Baguelin, M., Bhatt, S., Boonyasiri, A., Brazeau, N. F., Cattarino, L., Cooper, L., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21358-2 · Cited by 602

Untitled work

Vul (2014) · doi:10.1111/cogs.12101 · Cited by 502

Statistical inference for noisy nonlinear ecological dynamic systems

Wood, S. N. (2010) · Nature · doi:10.1038/nature09319 · Cited by 575

Generalized Out-of-Distribution Detection: A Survey

Yang, J., Zhou, K., Li, Y., & Liu, Z. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02117-4 · Cited by 884

Bayesian inference in physics

von Toussaint, U. (2011) · Reviews of Modern Physics · doi:10.1103/revmodphys.83.943 · Cited by 442

An Introduction to Bayesian Hypothesis Testing for Management Research

Andraszewicz, S., Scheibehenne, B., Rieskamp, J., Grasman, R. P. P. P., Verhagen, J., & Wagenmakers, E. (2014) · Journal of Management · doi:10.1177/0149206314560412 · Cited by 370

Particle Learning and Smoothing

Carvalho, C. M., Johannes, M. S., Lopes, H. F., & Polson, N. G. (2010) · Statistical Science · doi:10.1214/10-sts325 · Cited by 338

Nanoconnectomic upper bound on the variability of synaptic plasticity

Bartol, T. M., Bromer, C., Kinney, J. P., Chirillo, M. A., Bourne, J. N., Harris, K. M., & B, T. J. S. A. (2015) · eLife · doi:10.7554/elife.10778 · Cited by 341

Spatial and Spatio-Temporal Log-Gaussian Cox Processes: Extending the Geostatistical Paradigm

Diggle, P. J., Moraga, P., Rowlingson, B., & Taylor, B. M. (2013) · Statistical Science · doi:10.1214/13-sts441 · Cited by 276

From cosmic deceleration to acceleration: new constraints from SN Ia and BAO/CMB

Giostri, R., dos Santos, M. V., Waga, I., Reis, R. R. R., Calvão, M. O., & Lago, B. L. (2012) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2012/03/027 · Cited by 261

Fully Bayesian Spatio-Temporal Modeling of FMRI Data

Woolrich, M. W., Jenkinson, M., Brady, J. M., & Smith, S. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.823065 · Cited by 248

Soil Texture Drives Responses of Soil Respiration to Precipitation Pulses in the Sonoran Desert: Implications for Climate Change

Cable, J. M., Ogle, K., Williams, D. G., Weltzin, J. F., & Huxman, T. E. (2008) · Ecosystems · doi:10.1007/s10021-008-9172-x · Cited by 233

A Bayesian Approach to Improve Estimate at Completion in Earned Value Management

Caron, F., Ruggeri, F., & Merli, A. (2012) · Project Management Journal · doi:10.1002/pmj.21303 · Cited by 218

Stage-Specific Expression Profiling of Drosophila Spermatogenesis Suggests that Meiotic Sex Chromosome Inactivation Drives Genomic Relocation of Testis-Expressed Genes

Vibranovski, M. D., Lopes, H. F., Karr, T. L., & Long, M. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000731 · Cited by 214

A survey of Monte Carlo methods for parameter estimation

Luengo, D., Martino, L., Bugallo, M. F., Elvira, V., & Särkkä, S. (2020) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-020-00675-6 · Cited by 238

Effective sample size for importance sampling based on discrepancy measures

Martino, L., Elvira, V., & Louzada, F. (2016) · Signal Processing · doi:10.1016/j.sigpro.2016.08.025 · Cited by 200

The EM Algorithm

Ng, S., Krishnan, T., & McLachlan, G. J. (2011) · Springer eBooks · doi:10.1007/978-3-642-21551-3_6 · Cited by 198

Trends in the average temperature in Finland, 1847–2013

Mikkonen, S. J., Laine, M., Mäkelä, H. M., Gregow, H., Tuomenvirta, H., Lahtinen, M., & Laaksonen, A. (2014) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-014-0992-2 · Cited by 188

Estimating Risk for Death from Coronavirus Disease, China, January–February 2020

Mizumoto, K., & Chowell, G. (2020) · Emerging infectious diseases · doi:10.3201/eid2606.200233 · Cited by 188

Accelerated expansion from ghost-free bigravity: a statistical analysis with improved generality

Akrami, Y., Koivisto, T., & Sandstad, M. (2013) · Journal of High Energy Physics · doi:10.1007/jhep03(2013)099 · Cited by 191

Real-Time Detection of False Data Injection in Smart Grid Networks: An Adaptive CUSUM Method and Analysis

Huang, Y., Tang, J., Cheng, Y., Li, H., Campbell, K. A., & Han, Z. (2014) · IEEE Systems Journal · doi:10.1109/jsyst.2014.2323266 · Cited by 179

A Fuzzy Probability Bayesian Network Approach for Dynamic Cybersecurity Risk Assessment in Industrial Control Systems

Zhang, Q., Zhou, C., Tian, Y., Xiong, N. N., Qin, Y., & Hu, B. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2768998 · Cited by 178

Dynamic models of choice

Heathcote, A., Lin, Y.-S., Reynolds, A., Strickland, L. J. G., Gretton, M. B., & Matzke, D. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1067-y · Cited by 186

Liquidity in the Futures Pits: Inferring Market Dynamics from Incomplete Data

Hasbrouck, J. (2004) · Journal of Financial and Quantitative Analysis · doi:10.1017/s0022109000003082 · Cited by 170

Bayesian learning for neural networks: an algorithmic survey

Magris, M., & Iosifidis, A. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10443-1 · Cited by 182

Nonparametric reconstruction of the dark energy equation of state

Holsclaw, T., Ujjaini Alam, A., Sansó, B., Lee, H., Heitmann, K., Habib, S., & Higdon, D. (2010) · Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields · doi:10.1103/physrevd.82.103502 · Cited by 155

Epidemiology of the silent polio outbreak in Rahat, Israel, based on modeling of environmental surveillance data

Brouwer, A. F., Eisenberg, J. N. S., Pomeroy, C. D., Shulman, L. M., Hindiyeh, M. Y., Manor, Y., Grotto, I., Koopman, J. S., & Eisenberg, M. C. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1808798115 · Cited by 151

A semantics aware approach to automated reverse engineering unknown protocols

Wang, Y., Yun, X., Shafiq, M. Z., Wang, L., Liu, A. X., Zhang, Z., Yao, D. D., Zhang, Y., & Guo, L. (2012) · IEEE International Conference on Network Protocols (ICNP) · doi:10.1109/icnp.2012.6459963 · Cited by 148

The Structure of Large-Scale Synchronized Firing in Primate Retina

Shlens, J., Field, G. D., Gauthier, J. L., Greschner, M., Sher, A., Litke, A. M., & Chichilnisky, E. J. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.5187-08.2009 · Cited by 145

Personalisation in web computing and informatics: Theories, techniques, applications, and future research

Gao, M., Liu, K., & Wu, Z. (2009) · Information Systems Frontiers · doi:10.1007/s10796-009-9199-3 · Cited by 147

How to quantify support for and against the null hypothesis: A flexible WinBUGS implementation of a default Bayesian t test

Wetzels, R., Raaijmakers, J. G. W., Jakab, E., & Wagenmakers, E.-J. (2009) · Psychonomic Bulletin & Review · doi:10.3758/pbr.16.4.752 · Cited by 143

Bayesian inference in a class of partially identified models

Kline, B., & Tamer, E. (2016) · Quantitative Economics · doi:10.3982/qe399 · Cited by 142

Peekaboo: Learning-Based Multipath Scheduling for Dynamic Heterogeneous Environments

Wu, H., Alay, Ö., Brunström, A., Ferlin, S., & Caso, G. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3000365 · Cited by 143

A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges

Abdullah, A. A., Hassan, M. M., & Mustafa, Y. T. (2022) · IEEE Access · doi:10.1109/access.2022.3163384 · Cited by 143

Particle filters and Bayesian inference in financial econometrics

Lopes, H. F., & Tsay, R. S. (2010) · Journal of Forecasting · doi:10.1002/for.1195 · Cited by 137

Efficient Bayesian experimental design for contaminant source identification

Zhang, J., Zeng, L., Chen, C., Chen, D., & Wu, L. (2015) · Water Resources Research · doi:10.1002/2014wr015740 · Cited by 135

Transmission dynamics of COVID-19 in Wuhan, China: effects of lockdown and medical resources

Sun, G., Wang, S., Li, M., Li, L., Zhang, J., Zhang, W., Jin, Z., & Feng, G.-L. (2020) · Nonlinear Dynamics · doi:10.1007/s11071-020-05770-9 · Cited by 134

Changing philosophies and tools for statistical inferences in behavioral ecology

Garamszegi, L. Z., Calhim, S., Dochtermann, N. A., Hegyi, G., Hurd, P. L., Jørgensen, C., Kutsukake, N., Lajeunesse, M. J., Pollard, K. A., Schielzeth, H., et al. (2009) · Behavioral Ecology · doi:10.1093/beheco/arp137 · Cited by 133

Default “Gunel and Dickey” Bayes factors for contingency tables

Jamil, T., Ly, A., Morey, R. D., Love, J., Marsman, M., & Wagenmakers, E. (2016) · Behavior Research Methods · doi:10.3758/s13428-016-0739-8 · Cited by 136

A modelling approach for correcting reporting delays in disease surveillance data

Bastos, L. S., Economou, T., Gomes, M. F. C., Villela, D. A. M., Coelho, F. C., Cruz, O. G., Stoner, O., Bailey, T., & Codeço, C. T. (2019) · Statistics in Medicine · doi:10.1002/sim.8303 · Cited by 133

Bayesian inference of biochemical kinetic parameters using the linear noise approximation

Komorowski, M., Finkenstädt, B., Harper, C. V., & Rand, D. A. J. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-343 · Cited by 118

A Melanoma Brain Metastasis with a Donor-Patient Hybrid Genome following Bone Marrow Transplantation: First Evidence for Fusion in Human Cancer

Lazova, R. Z., LaBerge, G. S., Duvall, E., Spoelstra, N. S., Klump, V. R., Sznol, M., COOPER, D. L., Spritz, R. A., Chang, J. T., & Pawelek, J. M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066731 · Cited by 120

Early epidemiological assessment of the transmission potential and virulence of coronavirus disease 2019 (COVID-19) in Wuhan City, China, January–February, 2020

Mizumoto, K., Kagaya, K., & Chowell, G. (2020) · BMC Medicine · doi:10.1186/s12916-020-01691-x · Cited by 120

Bayesian inversion of data from effusive volcanic eruptions using physics‐based models: Application to Mount St. Helens 2004–2008

Anderson, K. R., & Segall, P. (2013) · Journal of Geophysical Research Solid Earth · doi:10.1002/jgrb.50169 · Cited by 118

Lipid and Peptide Diffusion in Bilayers: The Saffman–Delbrück Model and Periodic Boundary Conditions

Venable, R. M., Ingólfsson, H. I., Lerner, M. G., Perrin, B. S., Camley, B. A., Marrink, ‪. J., Brown, F. L. H., & Pastor, R. W. (2016) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6b09111 · Cited by 119

COVID-19 case fatality risk by age and gender in a high testing setting in Latin America: Chile, March–August 2020

Undurraga, E. A., Chowell, G., & Mizumoto, K. (2021) · Infectious Diseases of Poverty · doi:10.1186/s40249-020-00785-1 · Cited by 116

Bayesian power-spectrum inference for large-scale structure data

Jasche, J., Kitaura, F.-S., Wandelt, B. D., & Enßlin, T. A. (2010) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2010.16610.x · Cited by 113

INLA or MCMC? A tutorial and comparative evaluation for spatial prediction in log-Gaussian Cox processes

Taylor, B. M., & Diggle, P. J. (2013) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2013.788653 · Cited by 112

A State-of-the-Art Review of Bridge Inspection Planning: Current Situation and Future Needs

Abdallah, A. M., Atadero, R., & Ozbek, M. E. (2021) · Journal of Bridge Engineering · doi:10.1061/(asce)be.1943-5592.0001812 · Cited by 112

Different decision deficits impair response inhibition in progressive supranuclear palsy and Parkinson’s disease

Zhang, J., Rittman, T., Nombela, C., Fois, A. F., Coyle-Gilchrist, I. T. S., Barker, R. A., Hughes, L. E., & Rowe, J. B. (2015) · Brain · doi:10.1093/brain/awv331 · Cited by 109

Differential daytime and night‐time stomatal behavior in plants from North American deserts

Ogle, K., Lucas, R. W., Bentley, L. P., Cable, J. M., Barron‐Gafford, G. A., Griffith, A. B., Ignace, D. D., Jenerette, G. D., Tyler, A. P., Huxman, T. E., et al. (2012) · New Phytologist · doi:10.1111/j.1469-8137.2012.04068.x · Cited by 107

Nonparametric reconstruction of the dark energy equation of state from diverse data sets

Holsclaw, T., Ujjaini Alam, A., Sansó, B., Lee, H., Heitmann, K., Habib, S., & Higdon, D. (2011) · Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields · doi:10.1103/physrevd.84.083501 · Cited by 104

Computational methods for complex stochastic systems: a review of some alternatives to MCMC

Fearnhead, P. (2007) · Statistics and Computing · doi:10.1007/s11222-007-9045-8 · Cited by 104

Matter creation and cosmic acceleration

Ramos, R. O., dos Santos, M. V., & Waga, I. (2014) · Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields · doi:10.1103/physrevd.89.083524 · Cited by 107

The Random Walk Metropolis: Linking Theory and Practice Through a Case Study

Sherlock, C., Fearnhead, P., & Roberts, G. O. (2010) · Statistical Science · doi:10.1214/10-sts327 · Cited by 101

Linking statistical bias description to multiobjective model calibration

Reichert, P., & Schuwirth, N. (2012) · Water Resources Research · doi:10.1029/2011wr011391 · Cited by 99

Bayesian Estimation of Multinomial Processing Tree Models with Heterogeneity in Participants and Items

Matzke, D., Dolan, C. V., Batchelder, W. H., & Wagenmakers, E. (2013) · Psychometrika · doi:10.1007/s11336-013-9374-9 · Cited by 108

Analysing time-varying trends in stratospheric ozone time series using the state space approach

Laine, M., Latva-Pukkila, N., & Kyrola, E. (2014) · Atmospheric chemistry and physics · doi:10.5194/acp-14-9707-2014 · Cited by 103

Copula, marginal distributions and model selection: a Bayesian note

Silva, R. D. S., & Lopes, H. F. (2008) · Statistics and Computing · doi:10.1007/s11222-008-9058-y · Cited by 97

A Semantics-Aware Approach to the Automated Network Protocol Identification

Yun, X., Wang, Y., Zhang, Y., & Zhou, Y. (2015) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2014.2381230 · Cited by 96

Parameter estimation with gravitational waves

Christensen, N. L., & Meyer, R. (2022) · Reviews of Modern Physics · doi:10.1103/revmodphys.94.025001 · Cited by 115

Considering covariates in the covariance structure of spatial processes

Schmidt, A. M., Guttorp, P. M., & O’Hagan, A. (2011) · Environmetrics · doi:10.1002/env.1101 · Cited by 92

Geophysical imaging using trans-dimensional trees

Hawkins, R., & Sambridge, M. S. (2015) · Geophysical Journal International · doi:10.1093/gji/ggv326 · Cited by 92

2022 Review of Data-Driven Plasma Science

Anirudh, R., Archibald, R. K., Asif, M. S., Becker, M. M., Benkadda, S., Bremer, P., Budé, R. H. S., Chang, C. S., Chen, L., Churchill, R. M., et al. (2023) · IEEE Transactions on Plasma Science · doi:10.1109/tps.2023.3268170 · Cited by 105

An adaptive sparse-grid high-order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling

Zhang, G., Lu, D., Ye, M., Gunzburger, M., & Webster, C. (2013) · Water Resources Research · doi:10.1002/wrcr.20467 · Cited by 88

A joint analysis of influenza-associated hospitalizations and mortality in Hong Kong, 1998–2013

Wu, P., Presanis, A. M., Bond, H. S., Lau, E. H. Y., Fang, V. J., & Cowling, B. J. (2017) · Scientific Reports · doi:10.1038/s41598-017-01021-x · Cited by 82

Different Types and Acceptability of Psychotherapies for Acute Anxiety Disorders in Children and Adolescents: A Network Meta-analysis

Zhou, X., Zhang, Y., Furukawa, T. A., Cuijpers, P., Pu, J., Weisz, J. R., Yang, L., Hetrick, S. E., Del Giovane, C., Cohen, D., et al. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.3070 · Cited by 80

On using the cosmic microwave background shift parameter in tests of models of dark energy

Elgarøy, Ø., & Multamäki, T. (2007) · Astronomy and Astrophysics · doi:10.1051/0004-6361:20077292 · Cited by 81

Reconstructing population exposures to environmental chemicals from biomarkers: Challenges and opportunities

Georgopoulos, P. G., Sasso, A. F., Isukapalli, S., Lioy, P. J., Vallero, D. A., Okino, M. S., & Reiter, L. (2008) · Journal of Exposure Science & Environmental Epidemiology · doi:10.1038/jes.2008.9 · Cited by 79

Risk of death by age and gender from CoVID-19 in Peru, March-May, 2020

Munayco, C. V., Chowell, G., Tariq, A., Undurraga, E. A., & Mizumoto, K. (2020) · Aging · doi:10.18632/aging.103687 · Cited by 81

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

Bayesian joint modelling of longitudinal and time to event data: a methodological review

Alsefri, M., Sudell, M., García‐Fiñana, M., & Kolamunnage‐Dona, R. (2020) · BMC Medical Research Methodology · doi:10.1186/s12874-020-00976-2 · Cited by 76

The Role of Within-Trip Dynamics in Unplanned versus Planned Purchase Behavior

Gilbride, T. J., Inman, J. J., & Stilley, K. (2015) · Journal of Marketing · doi:10.1509/jm.13.0286 · Cited by 83

Bayesian Data—Model Integration in Plant Physiological and Ecosystem Ecology

Ogle, K., & Barber, J. J. (2007) · Progress in botany · doi:10.1007/978-3-540-72954-9_12 · Cited by 74

Interpretable Machine Learning for Catalytic Materials Design toward Sustainability

Xin, H., Mou, T., Pillai, H. S., Wang, S., & Huang, Y. (2023) · Accounts of Materials Research · doi:10.1021/accountsmr.3c00131 · Cited by 79

Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks

Li, L., Chang, J., Vakanski, A., Wang, Y., Yao, T., & Xian, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-61189-x · Cited by 76

MCMC for Imbalanced Categorical Data

Johndrow, J. E., Smith, A. D., Pillai, N. S., & Dunson, D. B. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2018.1505626 · Cited by 73

Statistical modeling methods: challenges and strategies

Henley, S. S., Golden, R. M., & Kashner, T. M. (2019) · Biostatistics & Epidemiology · doi:10.1080/24709360.2019.1618653 · Cited by 70

Multimodel-Inference in Comparative Analyses

Garamszegi, L. Z., & Mundry, R. (2014) · Springer eBooks · doi:10.1007/978-3-662-43550-2_12 · Cited by 70

Hydrogeophysical Characterization of Nonstationary DNAPL Source Zones by Integrating a Convolutional Variational Autoencoder and Ensemble Smoother

Kang, X., Kokkinaki, A., Kitanidis, P. K., Shi, X., Lee, J. H., Mo, S., & Wu, J. (2021) · Water Resources Research · doi:10.1029/2020wr028538 · Cited by 68

Uncertainty quantification in model verification and validation as applied to large scale historic masonry monuments

Atamturktur, S., Hemez, F., & Laman, J. A. (2012) · Engineering Structures · doi:10.1016/j.engstruct.2012.05.027 · Cited by 67

Full Bayesian Before-After Analysis of Safety Effects of Variable Speed Limit System

Pu, Z., Li, Z., Jiang, Y., & Wang, Y. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2961699 · Cited by 66

Analysis of COVID-19 transmission in Shanxi Province with discrete time imported cases

Li, M., Sun, G., Zhang, J., Zhao, Y., Pei, X., Li, L., Wang, Y., Zhang, W., Zhang, Z., & Jin, Z. (2020) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2020208 · Cited by 66

A NON-GAUSSIAN FAMILY OF STATE-SPACE MODELS WITH EXACT MARGINAL LIKELIHOOD

Gamerman, D., dos Santos, T. R., & Franco, G. C. (2013) · Journal of Time Series Analysis · doi:10.1111/jtsa.12039 · Cited by 62

Bayesian learning of chemisorption for bridging the complexity of electronic descriptors

Wang, S., Pillai, H. S., & Xin, H. (2020) · Nature Communications · doi:10.1038/s41467-020-19524-z · Cited by 67

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

Bahrami, P., Moghaddam, F. S., & James, L. A. (2022) · Energies · doi:10.3390/en15145247 · Cited by 68

A semiparametric Bayesian approach to extreme value estimation

do Nascimento, F. F., Gamerman, D., & Lopes, H. F. (2011) · Statistics and Computing · doi:10.1007/s11222-011-9270-z · Cited by 65

Panel data analysis: a survey on model-based clustering of time series

Frühwirth‐Schnatter, S. (2011) · Advances in Data Analysis and Classification · doi:10.1007/s11634-011-0100-0 · Cited by 64

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

A large-scale immuno-epidemiological simulation of influenza A epidemics

Lukens, S., DePasse, J. V., Rosenfeld, R., Ghedin, E., Mochan, E., Brown, S. T., Grefenstette, J. J., Burke, D. S., Swigon, D., & Clermont, G. (2014) · BMC Public Health · doi:10.1186/1471-2458-14-1019 · Cited by 63

Egg incubation effects generate positive correlations between size, speed and learning ability in young lizards

Amiel, J. J., Lindström, T., & Shine, R. (2013) · Animal Cognition · doi:10.1007/s10071-013-0665-4 · Cited by 65

Reconstruction of transcriptional dynamics from gene reporter data using differential equations

Finkenstädt, B., Heron, E. A., Komorowski, M., Edwards, K. D., Tang, S., Harper, C. V., Davis, J. R., White, M., Millar, A. J., & Rand, D. A. J. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn562 · Cited by 65

Markov chain Monte Carlo with neural network surrogates: application to contaminant source identification

Zhou, Z., & Tartakovsky, D. M. (2020) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-020-01888-9 · Cited by 61

Mathematical Parameters of the COVID-19 Epidemic in Brazil and Evaluation of the Impact of Different Public Health Measures

Cotta, R. M., Naveira‐Cotta, C. P., & Magal, P. (2020) · Biology · doi:10.3390/biology9080220 · Cited by 61

Strong constraints from COSINE-100 on the DAMA dark matter results using the same sodium iodide target

Adhikari, G., de Souza, E. B., Carlin, N., Choi, J. J., Choi, S., Djamal, M., Ezeribe, A. C., França, L. E., Ha, C. H., Hahn, I. S., et al. (2021) · Science Advances · doi:10.1126/sciadv.abk2699 · Cited by 61

Overview of Markov Chain Monte Carlo for Statistical Inference and its Application

Tao, B., & Chai, C. T. (2002) · Physica-Verlag eBooks · doi:10.1007/978-3-7908-1782-9_49

Markov Chain Monte Carlo - Innovations and Applications

Kendall, W. S., Liang, F., & Wang, J.-S. (2005) · Lecture notes series, Institute For Mathematical Sciences · doi:10.1142/9789812700919 · Cited by 34

Markov Chain Monte Carlo Methods

(2007) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420010718-13

Seeking Application of Markov Chain Monte Carlo in Different Fields

Chen, B. (2025) · Theoretical and Natural Science · doi:10.54254/2753-8818/2025.22222

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