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

Appropriate reduction of the posterior distribution in fully Bayesian inversions

Sato, D. S., Fukahata, Y., & Nozue, Y. (2022) · Geophysical Journal International · doi:10.1093/gji/ggac231 · Cited by 19

Capturing variability in pavement performance models from sufficient time-series predictors: a case study of the New Brunswick road network

Amador-Jiménez, L. E., & Mrawira, D. M. (2011) · Canadian Journal of Civil Engineering · doi:10.1139/l10-127 · Cited by 19

Dynamic Entry with Cross Product Spillovers: An Application to the Generic Drug IndustryPreprint

Gallant, A. R., HONG, H. P., & Khwaja, A. (2010) · SSRN Electronic Journal · doi:10.2139/ssrn.1674027 · Cited by 19

Monte Carlo Algorithms for the Partition Function and Information Rates of Two-Dimensional Channels

Molkaraie, M., & Loeliger, H. (2012) · IEEE Transactions on Information Theory · doi:10.1109/tit.2012.2212413 · Cited by 18

Interactions of periodic birth and shearing induce outbreak of Brucellosis in Inner Mongolia

Ma, X., Li, M., Zhang, J., Luo, X., & Sun, G. (2022) · International Journal of Biomathematics · doi:10.1142/s1793524522500437 · Cited by 18

A default Bayesian approach for regression on extremes

Cabras, S., Castellanos, M. E., & Gamerman, D. (2011) · Statistical Modelling · doi:10.1177/1471082x1001100606 · Cited by 18

Estimation of parameters from time traces originating from an Ornstein-Uhlenbeck process

Strey, H. H. (2019) · Physical review. E · doi:10.1103/physreve.100.062142 · Cited by 18

Uncertainty Quantification for Parameter Estimation and Response Prediction

Ricciardi, D. E., Chkrebtii, O., & Niezgoda, S. R. (2019) · Integrating materials and manufacturing innovation · doi:10.1007/s40192-019-00154-3 · Cited by 18

Spatial Structured Prediction Models: Applications, Challenges, and Techniques

Jiang, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2975584 · Cited by 19

Effects of population mobility on the COVID-19 spread in Brazil

Chagas, E. T. C., Barros, P. H., Cardoso-Pereira, I., Ponte, I. V., Ximenes, P., Figueiredo, F., Murai, F., da Silva, A. P. C., Almeida, J. M., Loureiro, A. A. F., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0260610 · Cited by 18

Main and Moderating Influence of Temperament Traits on the Association Between Intimate Partner Violence and Trauma Symptoms

Yalch, M., Levendosky, A., Bernard, N. K., & Bogat, G. (2017) · Journal of Interpersonal Violence · doi:10.1177/0886260515596978 · Cited by 20

Bayesian Merging of MISR and MODIS Aerosol Optical Depth Products Using Error Distributions From AERONET

Singh, M. K., Gautam, R., & Venkatachalam, P. A. (2017) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2734331 · Cited by 18

Hierarchical Markov Chain Monte Carlo Simulation for Modeling Transverse Cracks in Highway Pavements

Mills, L., Attoh-Okine, N., & McNeil, S. (2011) · Journal of Transportation Engineering · doi:10.1061/(asce)te.1943-5436.0000383 · Cited by 18

Tracking Flu Epidemics Using Google Flu Trends and Particle LearningPreprint

Dukić, V., Lopes, H. F., & Polson, N. (2009) · SSRN Electronic Journal · doi:10.2139/ssrn.1513705 · Cited by 18

A one-step Bayesian inversion framework for 3D reservoir characterization based on a Gaussian mixture model — A Norwegian Sea demonstration

Fjeldstad, T. M., Avseth, P., & Omre, H. (2020) · Geophysics · doi:10.1190/geo2020-0094.1 · Cited by 17

Pocket Crafter: a 3D generative modeling based workflow for the rapid generation of hit molecules in drug discovery

Shen, L., Fang, J. J., Liu, L., Yang, F., Jenkins, J. L., Kutchukian, P. S., & Wang, H. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00829-w · Cited by 17

The computational and neural substrates of individual differences in impulsivity under loss framework

Jiang, K.-Y., Zhao, G., Feng, Q., Guan, S., Im, H., Zhang, B., Wang, P.-C., Jia, X., Zhu, H., Zhu, Y., et al. (2024) · Human Brain Mapping · doi:10.1002/hbm.26808 · Cited by 17

Bayesian inference for a wave-front model of the neolithization of Europe

Baggaley, A. W., Sarson, G. R., Shukurov, A., Boys, R. J., & Golightly, A. (2012) · Physical Review E · doi:10.1103/physreve.86.016105 · Cited by 17

A Bayesian framework for the validation of models for subsurface flows: synthetic experiments

Akbarabadi, M., Borges, M. R., Jan, A., Pereira, F., & Piri, M. (2015) · Computational Geosciences · doi:10.1007/s10596-015-9538-z · Cited by 17

Bayesian Approach for the Seismic Fragility Estimation of a Containment Shell Based on the Formation of Through-Wall Cracks

Pujari, N. N., Ghosh, S., & Lala, S. (2015) · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering · doi:10.1061/ajrua6.0000840 · Cited by 17

The Poisson–Inverse-Gaussian regression model with cure rate: a Bayesian approach and its case influence diagnostics

Suzuki, A. K., Cancho, V. G., & Louzada, F. (2014) · Statistical Papers · doi:10.1007/s00362-014-0649-8 · Cited by 17

Storm Water Pollution Source Identification in Washington, DC, Using Bayesian Chemical Mass Balance Modeling

Sharifi, S., Haghshenas, M. M., Deksissa, T., Green, P. G., Hare, W., & Massoudieh, A. (2013) · Journal of Environmental Engineering · doi:10.1061/(asce)ee.1943-7870.0000809 · Cited by 17

Bayesian Risk Analysis

Czado, C., & Brechmann, E. C. (2014) · Springer eBooks · doi:10.1007/978-3-319-04486-6_8 · Cited by 17

Reflections on the Probability Space Induced by Moment Conditions with Implications for Bayesian Inference

Gallant, A. R. (2015) · Journal of Financial Econometrics · doi:10.1093/jjfinec/nbv008 · Cited by 17

Assessing an ensemble Kalman filter inference of Manning’s n coefficient of an idealized tidal inlet against a polynomial chaos-based MCMC

Siripatana, A., Mayo, T., Sraj, I., Knio, O., Dawson, C. N., Maître, O. P. L., & Hoteit, I. (2017) · Ocean Dynamics · doi:10.1007/s10236-017-1074-z · Cited by 17

Passive Vibration Control Using Viscoelastic Materials

Rade, D. A., Deü, J., Castello, D. A., de Lima, A. M. G., & Rouleau, L. (2019) · Mechanisms and machine science · doi:10.1007/978-3-030-13317-7_5 · Cited by 17

Attribute-Level Heterogeneity

Ebbes, P., Liechty, J., & Grewal, R. K. (2014) · Management Science · doi:10.1287/mnsc.2014.1898 · Cited by 17

Comparing Bayesian Spatial Conditional Overdispersion and the Besag–York–Mollié Models: Application to Infant Mortality Rates

Morales-Otero, M., & Núñez‐Antón, V. (2021) · Mathematics · doi:10.3390/math9030282 · Cited by 17

A spatio-temporal model of homicide in El Salvador

Carcach, C. A. (2015) · Crime Science · doi:10.1186/s40163-015-0033-6 · Cited by 17

BayesGCM: Software for Bayesian inference with the generalized context model

Vanpaemel, W. (2009) · Behavior Research Methods · doi:10.3758/brm.41.4.1111 · Cited by 17

Joint modeling of mixed skewed continuous and ordinal longitudinal responses: a Bayesian approach

Teimourian, M., Baghfalaki, T., Ganjali, M., & Berridge, D. M. (2015) · Journal of Applied Statistics · doi:10.1080/02664763.2015.1023557 · Cited by 17

Stochastic call center staffing with uncertain arrival, service and abandonment rates: A Bayesian perspective

Aktekin, T., & Ekin, T. (2016) · Naval Research Logistics (NRL) · doi:10.1002/nav.21716 · Cited by 17

The Sustained Influence of an Error on Future Decision-Making

Schiffler, B., Bengtsson, S., & Lundqvist, D. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.01077 · Cited by 15

Prognosis of Structural Damage Growth Via Integration of Physical Model Prediction and Bayesian Estimation

Liu, Y., Shuai, Q., Zhou, S., & Tang, J. (2017) · IEEE Transactions on Reliability · doi:10.1109/tr.2017.2713760 · Cited by 17

Joint decision-making of production and maintenance in mixed model assembly systems with delayed differentiation configurations

Guo, W., & Gu, X. (2019) · International Journal of Production Research · doi:10.1080/00207543.2019.1641641 · Cited by 17

A Bayes Factor for Replications of ANOVA Results

Harms, C. (2018) · The American Statistician · doi:10.1080/00031305.2018.1518787 · Cited by 19

Estimating the risk of 2019 Novel Coronavirus death during the course of the outbreak in China, 2020Preprint

Mizumoto, K., & Chowell, G. (2020) · medRxiv · doi:10.1101/2020.02.19.20025163 · Cited by 17

Macroevolutionary dynamics of climatic niche space

Quintero, I., Suchard, M. A., & Jetz, W. (2022) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2022.0091 · Cited by 15

What is the role of aerosol transmission in SARS-Cov-2 Omicron spread in Shanghai?

Sun, G., Ma, X., Zhang, Z., Liu, Q.-H., & Li, B.-L. (2022) · BMC Infectious Diseases · doi:10.1186/s12879-022-07876-4 · Cited by 16

Classical and Bayesian estimations of performance measures in a single server Markovian queueing system based on arrivals during service times

Singh, S. K., Cruz, F. R. B., Gomes, E. S., & Banik, A. D. (2023) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2022.2155789 · Cited by 16

Estimating Relative Changes of Metabolic Fluxes

Huang, L., Kim, D.-S., Liu, X., Myers, C. R., & Locasale, J. W. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003958 · Cited by 16

A Bayesian approach to rapid seismic vulnerability assessment at urban scale

Campostrini, G. P., Taffarel, S., Bettiol, G., Valluzzi, M. R., da Porto, F., & Modena, C. (2017) · International Journal of Architectural Heritage · doi:10.1080/15583058.2017.1370506 · Cited by 16

Transport Map sampling with PGD model reduction for fast dynamical Bayesian data assimilation

Rubio, P., Louf, F., & Chamoin, L. (2019) · International Journal for Numerical Methods in Engineering · doi:10.1002/nme.6143 · Cited by 16

The validity and consistency of continuous joystick response in perceptual decision-making

Szul, M. J., Bompas, A., Sumner, P., & Zhang, J. (2019) · Behavior Research Methods · doi:10.3758/s13428-019-01269-3 · Cited by 16

Bayesian approach to inverse time-harmonic acoustic scattering with phaseless far-field data

Yang, Z., Gui, X., Ming, J., & Hu, G. (2020) · Inverse Problems · doi:10.1088/1361-6420/ab82ee · Cited by 16

A Generalized Polynomial Chaos-Based Method for Efficient Bayesian Calibration of Uncertain Computational Models

Tagade, P., & Han-lim Choi, A. (2013) · Inverse Problems in Science and Engineering · doi:10.1080/17415977.2013.823411 · Cited by 16

Investigating the sensitivity of Gaussian processes to the choice of their correlation function and prior specifications

Schmidt, A. M., Conceição, M. D. F. D. G., & Moreira, G. A. (2008) · Journal of Statistical Computation and Simulation · doi:10.1080/00949650701231983 · Cited by 16

The Influence of Interpersonal Style on the Appraisal of Intimate Partner Violence

Yalch, M., & Levendosky, A. (2016) · Journal of Interpersonal Violence · doi:10.1177/0886260515576969 · Cited by 15

A Bayesian analysis of the Conway–Maxwell–Poisson cure rate model

Cancho, V. G., de Castro, M., & Rodrigues, J. (2010) · Statistical Papers · doi:10.1007/s00362-010-0326-5 · Cited by 16

Inference of Dynamic Generalized Linear Models: On‐Line Computation and Appraisal

Triantafyllopoulos, K. (2009) · International Statistical Review · doi:10.1111/j.1751-5823.2009.00087.x · Cited by 16

hmmSeq: A hidden Markov model for detecting differentially expressed genes from RNA-seq data

Cui, S., Guha, S., Ferreira, M. A. R., & Tegge, A. N. (2015) · The Annals of Applied Statistics · doi:10.1214/15-aoas815 · Cited by 16

Comparison of Numerical Approaches to Bayesian Updating

Rosić, B., Sýkora, J., Pajonk, O., Kučerová, A., & Matthies, H. G. (2016) · Computational methods in applied sciences · doi:10.1007/978-3-319-27996-1_16 · Cited by 16

Surrogate Models for Uncertainty Propagation and Sensitivity Analysis

Sargsyan, K. (2015) · Springer eBooks · doi:10.1007/978-3-319-11259-6_22-1 · Cited by 15

State space models with spatial deformation

Morales, F. E. C., Gamerman, D., & Paez, M. S. (2012) · Environmental and Ecological Statistics · doi:10.1007/s10651-012-0215-2 · Cited by 16

Introduction to MCMC

Pettitt, A. N., & Hincksman, C. (2012) · Wiley series in probability and statistics · doi:10.1002/9781118394472.ch2 · Cited by 16

Sampling with censored data: a practical guide

Ramos, P. L., Guzmán, D. C. F., Mota, A. L., Saavedra, D., Rodrigues, F. A., & Louzada, F. (2024) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2024.2409379 · Cited by 15

Contextual prediction errors reorganize naturalistic episodic memories in time

Yazin, F., Das, M., Banerjee, A., & Roy, D. (2021) · Scientific Reports · doi:10.1038/s41598-021-90990-1 · Cited by 19

Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology

Dabčević, Z., Škugor, B., Topić, J., & Deur, J. (2022) · Energies · doi:10.3390/en15114108 · Cited by 15

A Robust Learning Methodology for Uncertainty-Aware Scientific Machine Learning Models

Costa, É. A., Rebello, C. M., Fontana, M., Schnitman, L., & Nogueira, I. B. R. (2022) · Mathematics · doi:10.3390/math11010074 · Cited by 16

Virtual Stochastic Testing of Advanced Driver Assistance Systems

Olivares, S. P., Rebernik, N., Eichberger, A., & Stadlober, E. (2015) · Lecture notes in mobility · doi:10.1007/978-3-319-20855-8_3 · Cited by 15

Relative risk analysis of dengue cases using convolution extended into spatio-temporal model

Sani, A., Abapihi, B., Mukhsar, M., & Kadir, K. (2015) · Journal of Applied Statistics · doi:10.1080/02664763.2015.1043863 · Cited by 15

Bayes statistical decisions with random fuzzy data—an application in reliability

Hryniewicz, O. (2015) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2015.08.011 · Cited by 15

Bayesian inversion for the filtered flow at the Earth's core‐mantle boundary

Baerenzung, J., Holschneider, M., & Lesur, V. (2014) · Journal of Geophysical Research Solid Earth · doi:10.1002/2013jb010358 · Cited by 15

Bayesian approach to identify Hammerstein–Wiener non‐linear model in presence of noise and disturbance

Esmaeilani, L., Ghaisari, J., & Bagherzadeh, M. A. (2018) · IET Control Theory and Applications · doi:10.1049/iet-cta.2018.5562 · Cited by 15

Estimation of Surface Turbulent Fluxes From Land Surface Moisture and Temperature Via a Variational Data Assimilation Framework

Abdolghafoorian, A., & Farhadi, L. (2019) · Water Resources Research · doi:10.1029/2018wr024580 · Cited by 15

Bayesian Inference for Multidimensional Scaling Representations with Psychologically Interpretable Metrics

Gronau, Q. F., & Lee, M. D. (2020) · Computational Brain & Behavior · doi:10.1007/s42113-020-00082-y · Cited by 15

Geometric Morphometric Data Augmentation Using Generative Computational Learning Algorithms

Courtenay, L. A., & González‐Aguilera, D. (2020) · Applied Sciences · doi:10.3390/app10249133 · Cited by 17

Deciphering the transmission dynamics of COVID-19 in India: optimal control and cost effective analysis

Bajiya, V. P., Bugalia, S., Tripathi, J. P., & Martcheva, M. (2022) · Journal of Biological Dynamics · doi:10.1080/17513758.2022.2116493 · Cited by 14

Bayesian longitudinal item response modeling with restricted covariance pattern structures

Azevedo, C., Fox, J., & de Andrade, D. F. (2014) · Statistics and Computing · doi:10.1007/s11222-014-9518-5 · Cited by 15

Surveillance of dengue vectors using spatio-temporal Bayesian modeling

da Costa, A. C. C., Codeço, C. T., Honório, N. A., Pereira, G. R., Pinheiro, C. F. N., & Nobre, A. A. (2015) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-015-0219-6 · Cited by 15

A Bayesian approach for shipboard lifetime wave load spectrum updating

Zhu, J., & Collette, M. (2016) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2016.1165709 · Cited by 15

Introduction to Markov Chains

Bonamente, M. (2016) · Graduate texts in physics · doi:10.1007/978-1-4939-6572-4_15 · Cited by 15

Bayesian methods for addressing long-standing problems in associative learning: The case of PREE

Blanco, F., & Morís, J. (2017) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470218.2017.1358292 · Cited by 16

A skew-normal dynamic linear model and Bayesian forecasting

Arellano-Valle, R. B., Contreras‐Reyes, J. E., Quintero, F. O. L., & Valdebenito, A. (2018) · Computational Statistics · doi:10.1007/s00180-018-0848-1 · Cited by 15

Influence of Betrayal Trauma on Pathological Narcissism

Yalch, M. M., & Levendosky, A. A. (2019) · Journal of Aggression Maltreatment & Trauma · doi:10.1080/10926771.2019.1627685 · Cited by 15

Empirical Investigation of Continuous Logit for Departure Time Choice with Bayesian Methods

Lemp, J., & Kockelman, K. M. (2010) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2165-07 · Cited by 15

A drift diffusion model analysis of age-related impact on multisensory decision-making processes

Bolam, J., Diaz, J. A., Andrews, M. W., Coats, R. O., Philiastides, M. G., Astill, S. L., & Delis, I. (2024) · Scientific Reports · doi:10.1038/s41598-024-65549-5 · Cited by 15

Closing nature connectedness to foster environmental culture: investigating urban residents’ utilization and contribution to parks in Vietnam

Khuc, Q. V., Tran, M., Thinh, N. A., Lich, H. K., Dang, T., Nguyen, T. M. H., & Tran, D. T. (2024) · Discover Sustainability · doi:10.1007/s43621-024-00350-y · Cited by 22

Uncovering Load-Altering Attacks Against $N-1$ Secure Power Grids: A Rare-Event Sampling Approach

Goodridge, M. P., Lakshminarayana, S., & Zocca, A. (2024) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2024.3419725 · Cited by 14

Bayesian Multi-Scale Spatio-Temporal Modeling of Precipitation in the Indus Watershed

Christensen, M. F., Heaton, M. J., Rupper, S., Reese, C. S., & Christensen, W. F. (2019) · Frontiers in Earth Science · doi:10.3389/feart.2019.00210 · Cited by 14

Reaction time in ankle movements: a diffusion model analysis

Michmizos, K. P., & Krebs, H. I. (2014) · Experimental Brain Research · doi:10.1007/s00221-014-4032-8 · Cited by 15

A spatio‐temporal absorbing state model for disease and syndromic surveillance

Heaton, M. J., Banks, D. L., Zou, J., Karr, A. F., Datta, G. S., Lynch, J., & Vera, F. (2012) · Statistics in Medicine · doi:10.1002/sim.5350 · Cited by 14

Bayesian inference in measurement error models for replicated data

de Castro, M., Bolfarine, H., & Galea, M. (2012) · Environmetrics · doi:10.1002/env.2179 · Cited by 14

Bayesian time‐varying quantile regression to extremes

do Nascimento, F. F., & Bourguignon, M. (2019) · Environmetrics · doi:10.1002/env.2596 · Cited by 14

Static Posterior Inference of Bayesian Probabilistic Programming via Polynomial Solving

Wang, P., Yang, T., Fu, H., Li, G., & Ong, C.-H. L. (2024) · Proceedings of the ACM on Programming Languages · doi:10.1145/3656432 · Cited by 13

A temporal switch model for estimating transcriptional activity in gene expression

Jenkins, D. J., Finkenstädt, B., & Rand, D. A. J. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt111 · Cited by 13

MARS as an alternative approach of Gaussian graphical model for biochemical networks

Ayyıldız, E., Ağraz, M., & Purutçuoğlu, V. (2016) · Journal of Applied Statistics · doi:10.1080/02664763.2016.1266465 · Cited by 13

There is Nothing Magical about Bayesian Statistics: An Introduction to Epistemic Probabilities in Data Analysis for Psychology Starters

Świątkowski, W., & Carrier, A. (2020) · Basic and Applied Social Psychology · doi:10.1080/01973533.2020.1792297 · Cited by 15

A Hybrid Gibbs Sampler for Edge-Preserving Tomographic Reconstruction with Uncertain View Angles

Uribe, F., Bardsley, J. M., Dong, Y., Hansen, P. C., & Riis, N. A. B. (2022) · SIAM/ASA Journal on Uncertainty Quantification · doi:10.1137/21m1412268 · Cited by 13

The Impact of Quarantine and Medical Resources on the Control of COVID-19 in Wuhan based on a Household Model

Feng, S., Zhang, J., Li, J., Luo, X., Zhu, H., Li, M. Y., & Jin, Z. (2022) · Bulletin of Mathematical Biology · doi:10.1007/s11538-021-00989-y · Cited by 13

Horseshoe Priors for Edge-Preserving Linear Bayesian Inversion

Uribe, F., Dong, Y., & Hansen, P. C. (2023) · SIAM Journal on Scientific Computing · doi:10.1137/22m1510364 · Cited by 13

A general approach to Bayesian portfolio optimization

Bade, A., Frahm, G., & Jaekel, U. (2008) · Mathematical Methods of Operations Research · doi:10.1007/s00186-008-0271-4 · Cited by 13

Multi‐variate stochastic volatility modelling using Wishart autoregressive processes

Triantafyllopoulos, K. (2011) · Journal of Time Series Analysis · doi:10.1111/j.1467-9892.2011.00738.x · Cited by 13

Regional variations in the European Neolithic dispersal: the role of the coastlines

Henderson, D. A., Baggaley, A. W., Shukurov, A., Boys, R. J., Sarson, G. R., & Golightly, A. (2014) · Antiquity · doi:10.1017/s0003598x00115467 · Cited by 13

Evaluation of Multivariate Poisson Log Normal Bayesian Methods for Before-After Road Safety Evaluations

Lan, B., & Persaud, B. N. (2012) · Journal of Transportation Safety & Security · doi:10.1080/19439962.2011.649194 · Cited by 13

An application of the generalized Poisson difference distribution to the Bayesian modelling of football scores

Shahtahmassebi, G., & Moyeed, R. A. (2016) · Statistica Neerlandica · doi:10.1111/stan.12087 · Cited by 13

Bayesian Inference in the Multinomial Logit Model

Frühwirth‐Schnatter, S., & Fruhwirth, R. K. (2016) · Austrian Journal of Statistics · doi:10.17713/ajs.v41i1.186 · Cited by 13

Physiological responses of Chlorella sorokiniana to copper nanoparticles

Barreto, D. M., Tonietto, A. E., Amaral, C. D. B., Pulgrossi, R. C., Polpo, A., Nóbrega, J. A., & Lombardi, A. T. (2018) · Environmental Toxicology and Chemistry · doi:10.1002/etc.4332 · Cited by 13

Failure models driven by a self-correcting point process in earthquake occurrence modeling

Rotondi, R., & Varini, E. (2019) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-019-01663-5 · Cited by 13

River water quality modelling and simulation based on Markov Chain Monte Carlo computation and Bayesian inference model

Sahoo, M. M., & Patra, K. C. (2020) · African Journal of Science Technology Innovation and Development · doi:10.1080/20421338.2019.1692460 · Cited by 13

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)