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

Markov-chain Monte Carlo estimation of distributed Debye relaxations in spectral induced polarization

Keery, J., Binley, A. M., Elshenawy, A. M., & Clifford, J. (2012) · Geophysics · doi:10.1190/geo2011-0244.1 · Cited by 30

Experimental Identification of Thermophysical Properties in Heterogeneous Materials with Integral Transformation of Temperature Measurements from Infrared Thermography

Knupp, D. C., Naveira‐Cotta, C. P., Orlande, H. R. B., & Cotta, R. M. (2013) · Experimental Heat Transfer · doi:10.1080/08916152.2011.631079 · Cited by 30

A Bayesian framework for materials knowledge systems

Kalidindi, S. R. (2019) · MRS Communications · doi:10.1557/mrc.2019.56 · Cited by 29

A Bayesian destructive weighted Poisson cure rate model and an application to a cutaneous melanoma data

Rodrigues, J., Cancho, V. G., de Castro, M., & Balakrishnan, N. (2010) · Statistical Methods in Medical Research · doi:10.1177/0962280210391443 · Cited by 29

The flow at the Earth's core‐mantle boundary under weak prior constraints

Baerenzung, J., Holschneider, M., & Lesur, V. (2016) · Journal of Geophysical Research Solid Earth · doi:10.1002/2015jb012464 · Cited by 29

Bayesian analysis of the generalized gamma distribution using non-informative priors

Ramos, P. L., Achcar, J. A., Moala, F. A., Ramos, E., & Louzada, F. (2017) · Statistics · doi:10.1080/02331888.2017.1327532 · Cited by 29

Gaussian process emulators for spatial individual‐level models of infectious disease

Pokharel, G., & Deardon, R. (2016) · Canadian Journal of Statistics · doi:10.1002/cjs.11304 · Cited by 28

Surrogate Models for Uncertainty Propagation and Sensitivity Analysis

Sargsyan, K. (2017) · Springer eBooks · doi:10.1007/978-3-319-12385-1_22 · Cited by 29

Optimisation of a stochastic rock fracture model using Markov Chain Monte Carlo simulation

Xu, C., Dowd, P. A., & Wyborn, D. (2013) · Mining Technology Transactions of the Institutions of Mining and Metallurgy · doi:10.1179/1743286312y.0000000023 · Cited by 28

Dynamic survival models with spatial frailty

Bastos, L. S., & Gamerman, D. (2006) · Lifetime Data Analysis · doi:10.1007/s10985-006-9020-2 · Cited by 28

State and parameter estimation using Monte Carlo evaluation of path integrals

Quinn, J. C., & Abarbanel, H. D. I. (2010) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.690 · Cited by 28

Temporal variability in hydrology modifies the influence of geomorphology on wetland distribution along a desert stream

Dong, X., Grimm, N. B., Ogle, K., & Franklin, J. (2015) · Journal of Ecology · doi:10.1111/1365-2745.12450 · Cited by 28

Proper Generalized Decomposition model reduction in the Bayesian framework for solving inverse heat transfer problems

Berger, J., Orlande, H. R. B., & Mendes, N. (2016) · Inverse Problems in Science and Engineering · doi:10.1080/17415977.2016.1160395 · Cited by 28

Conditional predictive inference for online surveillance of spatial disease incidence

Vallet, A. C., & Lawson, A. B. (2011) · Statistics in Medicine · doi:10.1002/sim.4340 · Cited by 28

Influence on disease spread dynamics of herd characteristics in a structured livestock industry

Lindström, T., Lewerin, S. S., & Wennergren, U. (2011) · Journal of The Royal Society Interface · doi:10.1098/rsif.2011.0625 · Cited by 28

Accelerated failure identification sampling for probability analysis of rare events

Wang, Z., & Wang, P. (2016) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-016-1405-6 · Cited by 28

Bayesian Model Choice in Cumulative Link Ordinal Regression Models

McKinley, T. J., Morters, M. K., & Wood, J. L. N. (2015) · Bayesian Analysis · doi:10.1214/14-ba884 · Cited by 26

Scalable nonlinear programming framework for parameter estimation in dynamic biological system models

Shin, S., Venturelli, O. S., & Zavala, V. M. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006828 · Cited by 27

The detection and correction of various faulty sensors in a photovoltaic thermal heat pump system

Wang, P., Li, J., Yoon, S., Zhao, T., & Yu, Y. (2020) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2020.115347 · Cited by 27

Cognitive aging and verbal labeling in continuous visual memory

Forsberg, A., Johnson, W., & Logie, R. H. (2020) · Memory & Cognition · doi:10.3758/s13421-020-01043-3 · Cited by 45

A Review of Latent Space Models for Social Networks

Sosa, J. C., & Buitrago, L. (2021) · Revista Colombiana de Estadística · doi:10.15446/rce.v44n1.89369 · Cited by 27

Estimating the number of people with hepatitis C virus who have ever injected drugs and have yet to be diagnosed: an evidence synthesis approach for Scotland

Prevost, T. C., Presanis, A. M., Taylor, A., Goldberg, D. J., Hutchinson, S. J., & Angelis, D. D. (2015) · Addiction · doi:10.1111/add.12948 · Cited by 27

Bayesian spatio‐temporal models based on discrete convolutions

́o, B. S., Schmidt, A. M., & Nobre, A. A. (2008) · Canadian Journal of Statistics · doi:10.1002/cjs.5550360205 · Cited by 27

Transfer functions in dynamic generalized linear models

Alves, M. B., Gamerman, D., & Ferreira, M. A. R. (2010) · Statistical Modelling · doi:10.1177/1471082x0801000102 · Cited by 27

Bayesian cure rate models induced by frailty in survival analysis

de Souza, D., Cancho, V. G., Rodrigues, J., & Balakrishnan, N. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280217708671 · Cited by 27

Majorize–Minimize Adapted Metropolis–Hastings Algorithm

Marnissi, Y., Chouzenoux, É., Benazza‐Benyahia, A., & Pesquet, J.-C. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.2983150 · Cited by 27

Flexible covariate representations for extremes

Zanini, E., Eastoe, E., Jones, M. J., Randell, D., & Jonathan, P. (2020) · Environmetrics · doi:10.1002/env.2624 · Cited by 27

Gate-based quantum computing for protein design

Khatami, M. H., Mendes, U. C., Wiebe, N., & Kim, P. M. (2023) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1011033 · Cited by 26

Comparing and Blending Regional Climate Model Predictions for the American Southwest

Salazar, E., Sansó, B., Finley, A. O., Hammerling, D., Steinsland, I., Wang, X., & Delamater, P. L. (2011) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1007/s13253-011-0074-6 · Cited by 24

A modified particle filter for parameter identification with unknown inputs

Wan, Z., Wang, T., Li, S., & Zhang, Z. (2018) · Structural Control and Health Monitoring · doi:10.1002/stc.2268 · Cited by 26

The Connection between Bayesian Inference and Information Theory for Model Selection, Information Gain and Experimental Design

Oladyshkin, S., & Nowak, W. (2019) · Entropy · doi:10.3390/e21111081 · Cited by 27

BANDITS: Bayesian differential splicing accounting for sample-to-sample variability and mapping uncertainty

Tiberi, S., & Robinson, M. D. (2020) · Genome biology · doi:10.1186/s13059-020-01967-8 · Cited by 25

Does Smooth Ambiguity Matter for Asset Pricing?

Gallant, A. R., Jahan‐Parvar, M. R., & Liu, H. (2018) · Review of Financial Studies · doi:10.1093/rfs/hhy118 · Cited by 26

Metropolis Sampling

Martino, L., & Elvira, V. (2017) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat07951 · Cited by 29

A Bayesian Latent Group Analysis for Detecting Poor Effort in the Assessment of Malingering

Ortega, A., Wagenmakers, E., Lee, M. D., Markowitsch, H. J., & Piefke, M. (2012) · Archives of Clinical Neuropsychology · doi:10.1093/arclin/acs038 · Cited by 25

Multivariate Beta Regression with Application in Small Area Estimation

de Souza, D. F., & Moura, F. A. S. (2016) · Journal of Official Statistics · doi:10.1515/jos-2016-0038 · Cited by 25

Mothering under political violence: Post‐traumatic symptoms, observed maternal parenting practices and child externalising behaviour

Zamir, O., Gewirtz, A. H., Dekel, R., Lavi, T., & Tangir, G. (2018) · International Journal of Psychology · doi:10.1002/ijop.12557 · Cited by 30

Neuro-cognitive models of single-trial EEG measures describe latent effects of spatial attention during perceptual decision making

Ghaderi-Kangavari, A., Rad, J. A., Parand, K., & Nunez, M. D. (2022) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2022.102725 · Cited by 29

A sense of risk: Responses to crowdfunding risk disclosures

Shrestha, P., Thewissen, J., Arslan‐Ayaydin, Ö., & Parhankangas, A. (2023) · Strategic Entrepreneurship Journal · doi:10.1002/sej.1480 · Cited by 24

Modeling Macroinvertebrate Community Dynamics in Stream Mesocosms Contaminated with a Pesticide

Kattwinkel, M., Reichert, P., Rüegg, J., Liess, M., & Schuwirth, N. (2016) · Environmental Science & Technology · doi:10.1021/acs.est.5b04068 · Cited by 25

An Auxiliary Variable Method for Markov Chain Monte Carlo Algorithms in High Dimension

Marnissi, Y., Chouzenoux, É., Benazza‐Benyahia, A., & Pesquet, J.-C. (2018) · Entropy · doi:10.3390/e20020110 · Cited by 25

Bayesian modeling of multivariate spatial binary data with applications to dental caries

Bandyopadhyay, D., Reich, B. J., & Slate, E. H. (2009) · Statistics in Medicine · doi:10.1002/sim.3647 · Cited by 25

A new multivariate count data model to study multi-category physician prescription behavior

Dong, X., Chintagunta, P. K., & Manchanda, P. (2011) · Quantitative Marketing and Economics · doi:10.1007/s11129-011-9102-7 · Cited by 25

An unsupervised Bayesian hierarchical method for medical fraud assessment

Ekin, T., Lakomski, G., & Musal, R. M. (2019) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11408 · Cited by 24

A data-driven prioritisation framework to mitigate maintenance impact on passengers during metro line operation

Consilvio, A., Vignola, G., Arévalo, P. L., Gallo, F., Borinato, M., & Crovetto, C. (2024) · European Transport Research Review · doi:10.1186/s12544-023-00631-z · Cited by 29

Modeling strategic group dynamics: A hidden Markov approach

Ebbes, P., Grewal, R., & DeSarbo, W. S. (2010) · Quantitative Marketing and Economics · doi:10.1007/s11129-010-9081-0 · Cited by 24

A geostatistical Markov chain Monte Carlo inversion algorithm for electrical resistivity tomography

Aleardi, M., Vinciguerra, A., & Hojat, A. (2020) · Near Surface Geophysics · doi:10.1002/nsg.12133 · Cited by 24

Bayesian Chemical Mass Balance Method for Surface Water Contaminant Source Apportionment

Massoudieh, A., & Kayhanian, M. (2012) · Journal of Environmental Engineering · doi:10.1061/(asce)ee.1943-7870.0000645 · Cited by 24

Bayesian maximum entropy method for stochastic model updating using measurement data and statistical information

Wang, C., Yang, L., Xie, M., Valdebenito, M. A., & Beer, M. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.110012 · Cited by 24

Unique genetic and risk-factor profiles in clusters of major depressive disorder-related multimorbidity trajectories

Gézsi, A., der Auwera, S. V., Mäkinen, H., Eszlári, N., Hullám, G., Nagy, T., Bonk, S., González, R., Gonda, X., Garvert, L., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51467-7 · Cited by 23

Bayesian posterior predictive return levels for environmental extremes

Fawcett, L., & Green, A. C. (2018) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-018-1561-x · Cited by 23

Getting the Right Mix of Experts

Merrick, J. R. W. (2008) · Decision Analysis · doi:10.1287/deca.1080.0108 · Cited by 23

Reliability-based initial pavement performance deterioration modelling

Amador-Jiménez, L. E., & Mrawira, D. M. (2011) · International Journal of Pavement Engineering · doi:10.1080/10298436.2010.535538 · Cited by 23

Confronting Prior Convictions: On Issues of Prior Sensitivity and Likelihood Robustness in Bayesian Analysis

Lopes, H. F., & Tobias, J. L. (2011) · Annual Review of Economics · doi:10.1146/annurev-economics-111809-125134 · Cited by 23

Using Bayesian statistics in confirmatory clinical trials in the regulatory setting: a tutorial review

Lee, S. Y. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02235-0 · Cited by 27

Unsupervised joint deconvolution and segmentation method for textured images: a Bayesian approach and an advanced sampling algorithm

Vacar, C., & Giovannelli, J. (2019) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-018-0597-x · Cited by 22

Near Optimal Prediction from Relevant Components

Helland, I. S., Sæbø, S., & Tjelmeland, H. (2012) · Scandinavian Journal of Statistics · doi:10.1111/j.1467-9469.2011.00770.x · Cited by 22

Estimating population haplotype frequencies from pooled SNP data using incomplete database information

Pirinen, M. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp584 · Cited by 22

Developing Pairwise Preference-Based Personality Test and Experimental Investigation of Its Resistance to Faking Effect by Item Response Model

Usami, S., Sakamoto, A., Naito, J., & Abe, Y. (2016) · International Journal of Testing · doi:10.1080/15305058.2016.1145123 · Cited by 22

Experiential Avoidance, Dyadic Interaction and Relationship Quality in the Lives of Veterans and Their Partners

Zamir, O., Gewirtz, A. H., Labella, M. H., DeGarmo, D. S., & Snyder, J. J. (2017) · Journal of Family Issues · doi:10.1177/0192513x17698182 · Cited by 25

Gaussian Process–Based Inverse Uncertainty Quantification for TRACE Physical Model Parameters Using Steady-State PSBT Benchmark

Wang, C., Wu, X., & Kozłowski, T. (2018) · Nuclear Science and Engineering · doi:10.1080/00295639.2018.1499279 · Cited by 22

Betrayal Trauma and Dimensions of Borderline Personality Organization

Yalch, M., & Levendosky, A. (2014) · Journal of Trauma & Dissociation · doi:10.1080/15299732.2013.837135 · Cited by 21

Bayesian inference using WBDev: A tutorial for social scientists

Wetzels, R., Lee, M. D., & Wagenmakers, E. (2010) · Behavior Research Methods · doi:10.3758/brm.42.3.884 · Cited by 23

An overview of the current state of knowledge and technology on techniques and procedures for signal processing, analysis, and accurate inference for transportation noise and vibration

Burdzik, R., & Khan, D. (2025) · Measurement · doi:10.1016/j.measurement.2025.117314 · Cited by 24

Modeling heterogeneity of the level-1 error covariance matrix in multilevel models for single-case data

Baek, E., & Ferron, J. M. (2020) · Methodology · doi:10.5964/meth.2817 · Cited by 21

Data‐driven dynamical modelling of the transmission of African swine fever in a few places in China

Li, J., Jin, Z., Wang, Y., Sun, X., Xu, Q., Kang, J., Huang, B., & Zhu, H. (2021) · Transboundary and Emerging Diseases · doi:10.1111/tbed.14345 · Cited by 21

A Bayesian approach to estimating the long memory parameter

Chakraborty, S., Holan, S. H., & McElroy, T. (2009) · Bayesian Analysis · doi:10.1214/09-ba406 · Cited by 21

Integral Transforms and Bayesian Inference in the Identification of Variable Thermal Conductivity in Two-Phase Dispersed Systems

Naveira‐Cotta, C. P., Orlande, H. R. B., & Cotta, R. M. (2010) · Numerical Heat Transfer Part B Fundamentals · doi:10.1080/10407791003685106 · Cited by 21

Space-variable thermophysical properties identification in nanocomposites via integral transforms, Bayesian inference and infrared thermography

Knupp, D. C., Naveira‐Cotta, C. P., Ayres, J. V. C., Orlande, H. R. B., & Cotta, R. M. (2012) · Inverse Problems in Science and Engineering · doi:10.1080/17415977.2012.695358 · Cited by 21

Bayesian Simulation and Decision Analysis: An Expository Survey

Merrick, J. R. W. (2009) · Decision Analysis · doi:10.1287/deca.1090.0151 · Cited by 21

Weather SDM: estimating snow density with high precision using snow depth and local climate

Bruland, O., Færevåg, Å., Steinsland, I., Liston, G. E., & Sand, K. (2015) · Hydrology research · doi:10.2166/nh.2015.059 · Cited by 21

Model estimation of claim risk and premium for motor vehicle insurance by using Bayesian method

Sukono, S., Riaman, R., Lesmana, E., Wulandari, R. D., Napitupulu, H., & Supian, S. (2018) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/300/1/012027 · Cited by 21

Spatial variability in the risk of death from COVID-19 in Italy

Mizumoto, K., Dahal, S., & Chowell, G. (2020) · The International Journal of Tuberculosis and Lung Disease · doi:10.5588/ijtld.20.0262 · Cited by 21

Estimation of position-dependent transient heat source with the Kalman filter

da Fonseca, H. M., Orlande, H. R. B., & Fudym, O. (2012) · Inverse Problems in Science and Engineering · doi:10.1080/17415977.2012.712520 · Cited by 21

Extending Integrated Nested Laplace Approximation to a Class of Near‐Gaussian Latent Models

Martins, T. G., & Rue, H. (2014) · Scandinavian Journal of Statistics · doi:10.1111/sjos.12073 · Cited by 20

Reversible Jump Markov Chain Monte Carlo Method for Parameter Reduction in Claims Reserving

Verrall, R. J., & Wüthrich, M. V. (2012) · North American Actuarial Journal · doi:10.1080/10920277.2012.10590639 · Cited by 20

Bayesian model identification through harmonic balance method for hysteresis prediction in bolted joints

Miguel, L. P., Teloli, R. D. O., & da Silva, S. (2021) · Nonlinear Dynamics · doi:10.1007/s11071-021-06967-2 · Cited by 20

Efficient estimation and particle filter for max‐stable processes

Kunihama, T., Omori, Y., & Zhang, Z. (2011) · Journal of Time Series Analysis · doi:10.1111/j.1467-9892.2011.00740.x · Cited by 20

Sparse linear regression with beta process priors

Chen, B., Paisley, J. W., & Carin, L. (2010) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2010.5495400 · Cited by 20

Bayesian Updating of Parameters for a Sediment Entrainment Model via Markov Chain Monte Carlo

Wu, F., & Chen, C. C. (2008) · Journal of Hydraulic Engineering · doi:10.1061/(asce)0733-9429(2009)135:1(22 · Cited by 20

Time-varying extreme pattern with dynamic models

do Nascimento, F. F., Gamerman, D., & Lopes, H. F. (2015) · Test · doi:10.1007/s11749-015-0444-4 · Cited by 20

On structure-based priors in Bayesian geophysical inversion

Pasquale, G. D., & Linde, N. (2016) · Geophysical Journal International · doi:10.1093/gji/ggw458 · Cited by 20

Analysis of Permanence Time in Emotional States: A Case Study Using Educational Software

Reis, H. M., Alvares, D., Jaques, P. A., & Isotani, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91464-0_18 · Cited by 20

Non-point source evaluation of groundwater nitrate contamination from agriculture under geologic uncertainty

Ayub, R., Messier, K. P., Serre, M. L., & Mahinthakumar, G. (2019) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-019-01669-z · Cited by 20

The FGM Long-Term Bivariate Survival Copula Model: Modeling, Bayesian Estimation, and Case Influence Diagnostics

Louzada, F., Suzuki, A. K., & Cancho, V. G. (2013) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2012.725147 · Cited by 20

An Introduction to Markov Chain Monte CarloPreprint

Jespersen, N. S. (2010) · SSRN Electronic Journal · doi:10.2139/ssrn.1594971 · Cited by 20

Higher Minimum Quality Standards and Redistributive Effects on Consumer Welfare

Kotschedoff, M. J. W., & Pachali, M. J. (2020) · Marketing Science · doi:10.1287/mksc.2019.1172 · Cited by 19

Simulation of the Energy Efficiency Auction Prices via the Markov Chain Monte Carlo Method

López‐Gonzales, J. L., Souza, R. C., da Silva, F. L. C., Bustinza, N. C., Ibacache‐Pulgar, G., & Calili, R. F. (2020) · Energies · doi:10.3390/en13174544 · Cited by 19

Current Advances in Neural Networks

Gallego, V., & Insua, D. R. (2021) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-040220-112019 · Cited by 27

Physically meaningful uncertainty quantification in probabilistic wind turbine power curve models as a damage-sensitive feature

Mclean, J. H., Jones, M. R., O’Connell, B. J., Maguire, E., & Rogers, T. J. (2023) · Structural Health Monitoring · doi:10.1177/14759217231155379 · Cited by 19

A multiple group item response theory model with centered skew-normal latent trait distributions under a Bayesian framework

dos Santos, J. R. S., Azevedo, C., & Bolfarine, H. (2013) · Journal of Applied Statistics · doi:10.1080/02664763.2013.807331 · Cited by 19

Adaptive Quickest Estimation Algorithm for Smart Grid Network Topology Error

Huang, Y., Esmalifalak, M., Cheng, Y., Li, H., Campbell, K. A., & Han, Z. (2013) · IEEE Systems Journal · doi:10.1109/jsyst.2013.2260678 · Cited by 19

Stochastic Fracture Propagation Modelling for Enhanced Geothermal Systems

Xu, C., & Dowd, P. A. (2014) · Mathematical Geosciences · doi:10.1007/s11004-014-9542-1 · Cited by 19

Revisiting customer value analysis in a heterogeneous market

DeSarbo, W. S., Ebbes, P., Fong, D. Κ. H., & Snow, C. C. (2010) · Journal of Modelling in Management · doi:10.1108/17465661011026130 · Cited by 19

Constructing confidence intervals of extreme rainfall quantiles using Bayesian, bootstrap, and profile likelihood approaches

Chen, S., Li, Y., Shin, J.-Y., & Kim, T. (2015) · Science China Technological Sciences · doi:10.1007/s11431-015-5951-8 · Cited by 19

Evaluation of the information content of long-term wastewater characteristics data in relation to activated sludge model parameters

Alikhani, J., Takács, I., Al‐Omari, A., Murthy, S. N., & Massoudieh, A. (2017) · Water Science & Technology · doi:10.2166/wst.2017.004 · Cited by 19

PSI-NetVisor: Program semantic aware intrusion detection at network and hypervisor layer in cloud

Mishra, P., Pilli, E. S., Varadharajan, V., & Tupakula, U. K. (2017) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-169234 · Cited by 19

Opportunities for autonomous UAV in harsh environments

Scalea, R. A. L., Rodrigues, M., Osorio, D. P. M., Lima, C. H., Souza, R. D., Alves, H., & Branco, K. R. L. J. C. (2019) · International Symposium on Wireless Communication Systems · doi:10.1109/iswcs.2019.8877114 · Cited by 21

Bayesian analysis of the inverse generalized gamma distribution using objective priors

Ramos, P. L., Mota, A. L., Ferreira, P. H., Ramos, E., Tomazella, V. L. D., & Louzada, F. (2020) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2020.1830991 · Cited by 19

Quantifying Model Uncertainty in Inverse Problems via Bayesian Deep Gradient Descent

Barbano, R., Zhang, C., Arridge, S., & Jin, B. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412521 · Cited by 19

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

Markov chain Monte Carlo methods for statistical inference

Besag, J. E. (2004)