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

Estimating a DSGE model for Japan in a data-rich environment

Iiboshi, H., Matsumae, T., Namba, R., & Nishiyama, S. (2015) · Journal of the Japanese and International Economies · doi:10.1016/j.jjie.2015.02.001 · Cited by 9

Joint spatial Bayesian modeling for studies combining longitudinal and cross-sectional data

Lawson, A. B., Carroll, R. M., & Castro, M. C. (2014) · Statistical Methods in Medical Research · doi:10.1177/0962280214527383 · Cited by 9

Spatial measurement error in infectious disease models

Deardon, R., Habibzadeh, B., & Chung, H. Y. (2011) · Journal of Applied Statistics · doi:10.1080/02664763.2011.644522 · Cited by 9

A general long-term aging model with different underlying activation mechanisms: Modeling, Bayesian estimation, and case influence diagnostics

Suzuki, A. K., Barriga, G. D. C., Louzada, F., & Cancho, V. G. (2016) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2015.1053945 · Cited by 9

Bayesian inference for quantum state tomography

Gonçalves, D. S., Azevedo, C., Lavor, C., & Gomes-Ruggiero, M. A. (2017) · Journal of Applied Statistics · doi:10.1080/02664763.2017.1401049 · Cited by 9

Random Batch Methods for Classical and Quantum Interacting Particle Systems and Statistical Samplings

Jin, S., & Li, L. (2021) · Modeling and simulation in science, engineering & technology · doi:10.1007/978-3-030-93302-9_5 · Cited by 11

A Bivariate Multinomial Probit Model for Trip Scheduling: Bayesian Analysis of the Work Tour

Lemp, J., Kockelman, K. M., & Damien, P. (2012) · Transportation Science · doi:10.1287/trsc.1110.0397 · Cited by 9

Bayesian estimation of a bivariate copula using the Jeffreys prior

Guillotte, S., & Perron, F. (2012) · Bernoulli · doi:10.3150/10-bej345 · Cited by 9

Bayesian source identification of urban-scale air pollution from point and field concentration measurements

Aawar, E. A., Mohtar, S. E., Lakkis, I. A., Alduwais, A. K., & Hoteit, I. (2023) · Computational Geosciences · doi:10.1007/s10596-023-10206-5 · Cited by 9

Time series of count data: A review, empirical comparisons and data analysis

Franco, G. C., Migon, H. S., & Prates, M. O. (2019) · Brazilian Journal of Probability and Statistics · doi:10.1214/19-bjps437 · Cited by 8

A Dynamic Structural Equation Approach to Estimate the Short-Term Effects of Air Pollution on Human Health

Gamerman, D., Ippoliti, L., & Valentini, P. (2022) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/rssc.12554 · Cited by 8

Using Snapshot Data of Deficiency and Generic Deterioration to Predict the Degradation of Building Elements

Wickramasinghe, V., Dias, W. P. S., Tran, H., & Setunge, S. (2022) · Journal of Performance of Constructed Facilities · doi:10.1061/(asce)cf.1943-5509.0001743 · Cited by 8

Search for bosonic super-weakly interacting massive particles at COSINE-100

Adhikari, G., Carlin, N., Choi, J. J., Choi, S., Ezeribe, A. C., França, L. E., Ha, C. H., Hahn, I. S., Hollick, S. J., Jeon, E. J., et al. (2023) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.108.l041301 · Cited by 8

Bayesian analysis of accelerated life test under constrained randomization

Lv, S., Li, F., Wang, G., & Li, S. (2023) · Quality Engineering · doi:10.1080/08982112.2023.2255899 · Cited by 8

Cost-effectiveness of nonavalent HPV vaccination in the Netherlands

Palmer, C., Dolk, C., Sabale, U., Wang, W., & Saxena, K. (2024) · Expert Review of Vaccines · doi:10.1080/14760584.2024.2322543 · Cited by 8

Bayesian evidence synthesis to estimate HIV prevalence in men who have sex with men in Poland at the end of 2009

Rosińska, M., Gwiazda, P., Angelis, D. D., & Presanis, A. M. (2015) · Epidemiology and Infection · doi:10.1017/s0950268815002538 · Cited by 8

Interpersonal Style and Hypothetical Treatment Choice Among Survivors of Intimate Partner Violence

Yalch, M. M., Schroder, H. S., & Dawood, S. (2017) · Journal of Aggression Maltreatment & Trauma · doi:10.1080/10926771.2017.1325032 · Cited by 8

Vertical seismic profiling using double-beamforming processing of nonuniform anthropogenic seismic noise: The case study of Rittershoffen, Upper Rhine Graben, France

Lehujeur, M., Vergne, J., Maggi, A., & Schmittbuhl, J. (2017) · Geophysics · doi:10.1190/geo2017-0136.1 · Cited by 8

Bayesian inference of spatially varying Manning’s n coefficients in an idealized coastal ocean model using a generalized Karhunen-Loève expansion and polynomial chaos

Siripatana, A., Maître, O. P. L., Knio, O., Dawson, C. N., & Hoteit, I. (2020) · Ocean Dynamics · doi:10.1007/s10236-020-01382-4 · Cited by 8

Parameter estimation for X-ray scattering analysis with Hamiltonian Markov Chain Monte Carlo

Jiang, Z., Wang, J., Tirrell, M., de Pablo, J. J., & Chen, W. (2022) · Journal of Synchrotron Radiation · doi:10.1107/s1600577522003034 · Cited by 8

Analysis of presence-only data via exact Bayes, with model and effects identification

Moreira, G. A., & Gamerman, D. (2022) · The Annals of Applied Statistics · doi:10.1214/21-aoas1569 · Cited by 8

Truncated Log-concave Sampling for Convex Bodies with Reflective Hamiltonian Monte Carlo

Chalkis, A., Fisikopoulos, V., Papachristou, M., & Tsigaridas, E. (2023) · ACM Transactions on Mathematical Software · doi:10.1145/3589505 · Cited by 7

Analyzing space-time sensor network data under suppression and failure in transmission

Puggioni, G., & Gelfand, A. E. (2009) · Statistics and Computing · doi:10.1007/s11222-009-9133-z · Cited by 8

An estimation method for latent traits and population parameters in Nominal Response Model

Azevedo, C., & de Andrade, D. F. (2010) · Brazilian Journal of Probability and Statistics · doi:10.1214/09-bjps102 · Cited by 8

Belief-propagation-guided Monte-Carlo sampling

Decelle, A., & Krząkała, F. (2014) · Physical Review B · doi:10.1103/physrevb.89.214421 · Cited by 8

Bayesian methods in cost–effectiveness studies: objectivity, computation and other relevant aspects

Armero, C., García‐Donato, G., & López‐Quílez, A. (2009) · Health Economics · doi:10.1002/hec.1496 · Cited by 8

The Influence of Network Properties on the Synchronization of Kuramoto Oscillators Quantified by a Bayesian Regression Analysis

de Arruda, G. F., Peron, T., Andrade, M. G., Achcar, J. A., & Rodrigues, F. A. (2013) · Journal of Statistical Physics · doi:10.1007/s10955-013-0775-z · Cited by 9

A Closer Look at Degradation Models: Classical and Bayesian Approaches

Freitas, M. A., dos Santos, T. R., Pires, M. C., & Colosimo, E. A. (2009) · Birkhäuser Boston eBooks · doi:10.1007/978-0-8176-4924-1_11 · Cited by 8

A Sparse Grid Method for Bayesian Uncertainty Quantification with Application to Large Eddy Simulation Turbulence Models

Tran, H., Webster, C., & Zhang, G. (2016) · Lecture notes in computational science and engineering · doi:10.1007/978-3-319-28262-6_12 · Cited by 8

LIDA: A Land Integrated Data Assimilation Framework for Mapping Land Surface Heat and Evaporative Fluxes by Assimilating Space‐Borne Soil Moisture and Land Surface Temperature

Abdolghafoorian, A., & Farhadi, L. (2020) · Water Resources Research · doi:10.1029/2020wr027183 · Cited by 8

Mathematical Models of the Epidemiology andControl of Foot-and-Mouth Disease

Woolhouse, M. E. J. (2004) · CRC Press eBooks · doi:10.1201/9781420037968.ch13 · Cited by 8

Limits on WIMP Dark Matter with NaI(Tl) Crystals in Three Years of COSINE-100 Data

Yu, G. H., Carlin, N., Cho, J. Y., Choi, J. J., Choi, S., Ezeribe, A. C., França, L. E., Ha, C. H., Hahn, I. S., Hollick, S. J., et al. (2025) · Physical Review Letters · doi:10.1103/77p2-rnbw · Cited by 7

The Use of Official Statistics in Self-Selection Bias Modeling

Valle, L. D. (2016) · Journal of Official Statistics · doi:10.1515/jos-2016-0046 · Cited by 7

Multi‐Tracer Groundwater Dating in Southern Oman Using Bayesian Modeling

Rädle, V., Kersting, A., Schmidt, M., Ringena, L., Robertz, J., Aeschbach, W., Oberthaler, M., & Müller, T. (2022) · Water Resources Research · doi:10.1029/2021wr031776 · Cited by 4

Bayesian Ensemble Kalman Filter for Gaussian Mixture Models

Gryvill, H., Graña, D., & Tjelmeland, H. (2024) · Mathematical Geosciences · doi:10.1007/s11004-024-10160-7 · Cited by 7

A step towards efficient inference for trends in UK extreme temperatures through distributional linkage between observations and climate model data

Gabda, D., Tawn, J. A., & Brown, S. J. (2018) · Natural Hazards · doi:10.1007/s11069-018-3504-8 · Cited by 7

Identification of the tension force in cables with insulators

Rango, B. J., Serralunga, F. J., Piovan, M. T., Ballaben, J. S., & Rosales, M. B. (2019) · Meccanica · doi:10.1007/s11012-018-00941-w · Cited by 7

Estimation of the kidney metabolic heat generation rate

Orlande, H. R. B., Lutaif, N. A., & Gontijo, J. A. R. (2019) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.3224 · Cited by 7

A Causal Analysis of the Effect of Age and Sex Differences on Brain Atrophy in the Elderly Brain

Gómez‐Ramírez, J., Fernández‐Blázquez, M. A., & González-Rosa, J. J. (2022) · Life · doi:10.3390/life12101586 · Cited by 7

Bayesian modelling of integer-valued transfer function models

Pingal, A. C., & Chen, C. W. S. (2022) · Statistical Modelling · doi:10.1177/1471082x221075477 · Cited by 7

Multisensors Fusion for Trajectory Tracking Based on Variational Normalizing Flow

Tang, Q., Liang, J., & Zhu, F. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3289960 · Cited by 7

Probabilistic creep with the Wilshire–Cano–Stewart model

Hossain, M. A., Cano, J. A., & Stewart, C. M. (2023) · Fatigue & Fracture of Engineering Materials & Structures · doi:10.1111/ffe.14120 · Cited by 7

Hierarchical Bayesian Model Updating for Probabilistic Damage Identification

Behmanesh, I., Moaveni, B., Lombaert, G., & Papadimitriou, C. (2015) · Conference proceedings of the Society for Experimental Mechanics · doi:10.1007/978-3-319-15224-0_6 · Cited by 7

Identification of Properties of Stochastic Elastoplastic Systems

Rosić, B., & Matthies, H. G. (2012) · Computational methods in applied sciences · doi:10.1007/978-94-007-5134-7_14 · Cited by 7

On computational aspects of Bayesian spatial models: influence of the neighboring structure in the efficiency of MCMC algorithms

Mayrink, V. D., & Gamerman, D. (2009) · Computational Statistics · doi:10.1007/s00180-009-0153-0 · Cited by 7

Bayesian inference as a tool for analysis of first-principles calculations of complex materials: an application to the melting point of Ti2GaN

Davis, S. M., & Gutiérrez, G. (2013) · Modelling and Simulation in Materials Science and Engineering · doi:10.1088/0965-0393/21/7/075001 · Cited by 7

A full Bayesian partition model for identifying hypo- and hyper-methylated loci from single nucleotide resolution sequencing data

Wang, H., He, C. Z., Kushwaha, G., Xu, D. H., & Qiu, J. (2016) · BMC Bioinformatics · doi:10.1186/s12859-015-0850-3 · Cited by 7

On Fisher’s dispersion test for integer-valued autoregressive Poisson models with applications

Lee, S., Park, S., & Chen, C. W. S. (2016) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2016.1228970 · Cited by 7

A non-default fraction bivariate regression model for credit scoring: An application to Brazilian customer data

Cancho, V. G., Suzuki, A. K., Barriga, G. D. C., & Louzada, F. (2016) · Communications in Statistics Case Studies Data Analysis and Applications · doi:10.1080/23737484.2016.1243456 · Cited by 7

Analysing plant closure effects using time-varying mixture-of-experts Markov chain clustering

Frühwirth‐Schnatter, S., Pittner, S., Weber, A. M., & Winter‐Ebmer, R. (2018) · The Annals of Applied Statistics · doi:10.1214/17-aoas1132 · Cited by 7

Stochastic Mortality Modeling: Key Drivers and Dependent Residuals

Mavros, G., Cairns, A. J. G., Streftaris, G., & Kleinow, T. (2017) · North American Actuarial Journal · doi:10.1080/10920277.2017.1286992 · Cited by 7

Bayesian analysis of a density ratio model

Oliveira, V. D., & Kedem, B. (2017) · Canadian Journal of Statistics · doi:10.1002/cjs.11318 · Cited by 7

Statistical plant modeling and simulation in online advertising

Mardanlou, V., Karlsson, N., & Guo, J. (2017) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.2017.7963275 · Cited by 7

Combining ensemble Kalman filter and multiresolution analysis for efficient assimilation into adaptive mesh models

Siripatana, A., Giraldi, L., Maître, O. P. L., Knio, O., & Hoteit, I. (2019) · Computational Geosciences · doi:10.1007/s10596-019-09882-z · Cited by 7

Complementary Bayesian method of moments strategies

Gallant, A. R. (2020) · Journal of Applied Econometrics · doi:10.1002/jae.2758 · Cited by 7

Learning the Stress-Strain Relationships of Ultra-Thin Package Materials using a Bayesian Approach

Selvanayagam, C., Duong, P. L. T., & Raghavan, N. (2020) · IEEE Electronic Components and Technology Conference (ECTC) · doi:10.1109/ectc32862.2020.00257 · Cited by 7

Inverse Design of Substrate from Warpage Surrogate Model Using Global Optimisation Algorithms in Ultra-Thin Packages

Selvanayagam, C., Duong, P. L. T., & Raghavan, N. (2020) · IEEE Electronic Components and Technology Conference (ECTC) · doi:10.1109/ectc32862.2020.00360 · Cited by 7

A Bayesian model for multiple change point to extremes, with application to environmental and financial data

do Nascimento, F. F., & Silva, W. V. M. E. (2016) · Journal of Applied Statistics · doi:10.1080/02664763.2016.1254733 · Cited by 7

Bayesian residual analysis for spatially correlated data

Lobo, V. G. R., & Fonseca, T. C. O. (2019) · Statistical Modelling · doi:10.1177/1471082x18811529 · Cited by 7

A Bayesian Method for Material Identification of Composite Plates via Dispersion Curves

Haywood-Alexander, M., Dervilis, N., Keith Worden, B., Mills, R. S., Ladpli, P., & Rogers, T. J. (2022) · Sensors · doi:10.3390/s23010185 · Cited by 6

Bayesian Graphical Entity Resolution using Exchangeable Random Partition Priors

Marchant, N. G., Rubinstein, B. I. P., & Steorts, R. C. (2023) · Journal of Survey Statistics and Methodology · doi:10.1093/jssam/smac030 · Cited by 6

Spatial Analysis of HIV Determinants Among Females Aged 15–34 in KwaZulu Natal, South Africa: A Bayesian Spatial Logistic Regression Model

Chireshe, E., Chifurira, R., Chinhamu, K., Batidzirai, J. M., & Kharsany, A. B. M. (2025) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph22030446 · Cited by 7

Infinite product expansion of the Fokker–Planck equation with steady-state solution

Martin, R. J., Craster, R. V., & Kearney, M. J. (2015) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2015.0084 · Cited by 6

Dirichlet process mixture models with multiple modalities

Paisley, J. W., & Carin, L. (2009) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2009.4959908 · Cited by 6

Estimating the partition function of 2-D fields and the capacity of constrained noiseless 2-D channels using tree-based Gibbs sampling

Loeliger, H., & Molkaraie, M. (2009) · IEEE Information Theory Workshop (ITW) · doi:10.1109/itw.2009.5351455 · Cited by 6

Structural Deterioration Modeling Using Variational Inference

Dann, M. R., & Birkland, M. (2018) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000805 · Cited by 6

Prediction of leukocyte counts during paediatric acute lymphoblastic leukaemia maintenance therapy

Karppinen, S., Lohi, O., & Vihola, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-54492-5 · Cited by 7

A Bayesian stochastic generator to complement existing climate change scenarios: supporting uncertainty quantification in marine and coastal ecosystems

Mészáros, L., van der Meulen, F., Jongbloed, G., & Serafy, G. Y. E. (2020) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-020-01935-5 · Cited by 6

Contact network uncertainty in individual level models of infectious disease transmission

Almutiry, W. K., & Deardon, R. (2021) · Statistical Communications in Infectious Diseases · doi:10.1515/scid-2019-0012 · Cited by 6

Approximation error model (AEM) approach with hybrid methods in the forward-inverse analysis of the transesterification reaction in 3D-microreactors

Pontes, P. C., Junior, J. M. C., Naveira‐Cotta, C. P., & Tiwari, M. K. (2021) · Inverse Problems in Science and Engineering · doi:10.1080/17415977.2020.1870973 · Cited by 6

A Kalman particle filter for online parameter estimation with applications to affine models

He, J., Khedher, A., & Spreij, P. (2021) · Statistical Inference for Stochastic Processes · doi:10.1007/s11203-021-09239-3 · Cited by 6

Influence of statistical sample size on evaluation of overall strength of cement-treated soil column

Namikawa, T. (2021) · Canadian Geotechnical Journal · doi:10.1139/cgj-2020-0640 · Cited by 6

History Matching Complex 3D Systems Using Deep-Learning-Based Surrogate Flow Modeling and CNN-PCA Geological Parameterization

Tang, M., Liu, Y., & Durlofsky, L. J. (2021) · SPE Reservoir Simulation Conference · doi:10.2118/203924-ms · Cited by 6

Peripersonal Space from a multisensory perspective: the distinct effect of the visual and tactile components of Visuo-Tactile stimuli

Beccherle, M., Facchetti, S., Villani, F., Zanini, M., & Scandola, M. (2022) · Experimental Brain Research · doi:10.1007/s00221-022-06324-8 · Cited by 7

Bayesian inference for fitting cardiac models to experiments: estimating parameter distributions using Hamiltonian Monte Carlo and approximate Bayesian computation

Ramos, A. N., Fenton, F. H., & Cherry, E. M. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-022-02685-y · Cited by 6

Impact Assessment of Wet and Dry Spell on Agriculture Productivity of Chhattisgarh, India

Tandel, D., Verma, S., Kumar, K., & Verma, M. K. (2023) · Journal of Environmental Informatics Letters · doi:10.3808/jeil.202300108 · Cited by 6

A combined Markov Chain Monte Carlo and Levenberg–Marquardt inversion method for heterogeneous subsurface reservoir modeling

Fernandes, J. A., Azevedo, J. S., & Oliveira, S. P. (2024) · Discover Applied Sciences · doi:10.1007/s42452-024-06214-4 · Cited by 6

Single-Cell States in the Estrogen Response of Breast Cancer Cell Lines

Casale, F. P., Giurato, G., Nassa, G., Armond, J. W., Oates, C. J., Corà, D., Gamba, A., Mukherjee, S., Weisz, A., & Nicodemi, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0088485 · Cited by 6

Modification of existing shape factor models for self-compacting concrete strength by means of Bayesian updating techniques

Vurst, F. V. D., Caspeele, R., Desnerck, P., Schutter, G. D., & Peirs, J. (2013) · Materials and Structures · doi:10.1617/s11527-013-0222-2 · Cited by 6

Reflections on the Probability Space Induced by Moment Conditions with Implications for Bayesian Inference — Author Response to Comments: Table 1.

Gallant, A. R. (2016) · Journal of Financial Econometrics · doi:10.1093/jjfinec/nbv007 · Cited by 6

Classical-Equivalent Bayesian Portfolio Optimization for Electricity Generation Planning

Takada, H. H., Stern, J. M., Costa, O. L. V., & Ribeiro, C. D. O. (2018) · Entropy · doi:10.3390/e20010042 · Cited by 6

Probabilistic analysis of overall strength of a cement‐treated soil column considering statistical uncertainty and spatial variability

Namikawa, T. (2020) · International Journal for Numerical and Analytical Methods in Geomechanics · doi:10.1002/nag.3182 · Cited by 6

Manifold Optimization-Assisted Gaussian Variational Approximation

Zhong, B., Gao, J., Tran, M., & Gerlach, R. (2021) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2021.1923516 · Cited by 6

Hopping between distant basins

Goodridge, M. P., Moriarty, J., Vogrinc, J., & Zocca, A. (2022) · Journal of Global Optimization · doi:10.1007/s10898-022-01153-z · Cited by 6

Parameter estimation in high‐intensity focused ultrasound therapy

Cárdenas, L. D. L. R., Varón, L. A. B., & Pereira, W. C. D. A. (2022) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.3591 · Cited by 6

Bayesian inference of spectrometric data and validation with numerical simulations of plasma sheath diagnostics of a plasma focus discharge

Avaria, G., Clausse, A., Davis, S. M., Pavéz, C., Villalba, N., Cuadrado, O., Moreno, J., Ruiz, H. M., & Soto, L. (2022) · Scientific Reports · doi:10.1038/s41598-022-19764-7 · Cited by 6

An efficient sampling scheme for dynamic generalized models

Migon, H. S., Schmidt, A. M., Ravines, R. R., & Pereira, J. B. (2013) · Computational Statistics · doi:10.1007/s00180-013-0406-9 · Cited by 6

A multiple-try Metropolis–Hastings algorithm with tailored proposals

Luo, X., & Tjelmeland, H. (2019) · Computational Statistics · doi:10.1007/s00180-019-00878-y · Cited by 6

Using a non-homogeneous Poisson model with spatial anisotropy and change-points to study air pollution data

Rodrigues, E. R., Nicholls, G. K., Tarumoto, M. H., & Tzintzun, G. (2019) · Environmental and Ecological Statistics · doi:10.1007/s10651-019-00423-6 · Cited by 5

Count time series prediction using particle filters

Triantafyllopoulos, K., Shakandli, M. M., & Campbell, M. J. (2019) · Quality and Reliability Engineering International · doi:10.1002/qre.2534 · Cited by 6

Extreme Value Theory Applied to r Largest Order Statistics Under the Bayesian Approach

Silva, R. S., & do Nascimento, F. F. (2019) · Revista Colombiana de Estadística · doi:10.15446/rce.v42n2.70271 · Cited by 6

Fast inference for statistical inverse problems

Taddy, M. A., Lee, H. K. H., & Sansó, B. (2009) · Inverse Problems · doi:10.1088/0266-5611/25/8/085001 · Cited by 6

Uncertainty Quantification and B ayesian Inversion

Matthies, H. G. (2017) · doi:10.1002/9781119176817.ecm2071 · Cited by 6

Markov Chain Monte Carlo

Padmanabhan, S., & Ranjan, U. (2022) · Encyclopedia of earth sciences series/Encyclopedia of earth sciences · doi:10.1007/978-3-030-26050-7_191-1 · Cited by 6

A Good check on the Bayes factor

Sekulovski, N., Marsman, M., & Wagenmakers, E. (2024) · Behavior Research Methods · doi:10.3758/s13428-024-02491-4 · Cited by 5

Physics-informed neural network uncertainty assessment through Bayesian inference.

Costa, É. A., Rebello, C. M., Santana, V. V., & Nogueira, I. B. R. (2024) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2024.08.411 · Cited by 5

Bayesian performance assessment of existing concrete structures combining different types of information from inspections and monitoring

Caspeele, R., Botte, W., & Vereecken, E. (2025) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2025.2474700 · Cited by 5

Understanding Farmers’ Knowledge, Perceptions, and Adaptation Strategies to Climate Change in Eastern Rwanda

Rwema, M., Safari, B., Sylla, M. B., Roininen, L., & Laine, M. (2025) · Sustainability · doi:10.3390/su17156721 · Cited by 5

Alcohol-induced deaths in the United States across age, race, gender, geography, and the COVID-19 pandemic

Wong, T., Böttcher, L., Chou, T., & D'Orsogna, M. R. (2025) · PLOS Global Public Health · doi:10.1371/journal.pgph.0004623 · Cited by 5

Quantifying uncertainty of mineral prediction using a novel Bayesian deep learning framework

Liu, Y. (2025) · Artificial Intelligence in Geosciences · doi:10.1016/j.aiig.2025.100164 · Cited by 5

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)