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

Near Real Time Damage Diagnosis Using Surrogate Modeling and High Performance Computing

Warner, J. E., Zubair, M., & Ranjan, D. (2017) · AIAA Non-Deterministic Approaches Conference · doi:10.2514/6.2017-1563 · Cited by 2

Judd−Ofelt Parameters via Bayesian Inference

Silva, V. A., Silva, J. W., Morais, P. C., & Dantas, N. O. (2017) · Brazilian Journal of Physics · doi:10.1007/s13538-017-0549-1 · Cited by 2

A Metropolis algorithm to obtain co-sufficient samples with applications in conditional tests

dos Santos, J. D. O., & Filho, N. L. D. S. (2018) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2018.1458140 · Cited by 2

A Follower-Based Control Allocation in Multi-Agent Networks

Buzorgnia, D., & Aghdam, A. G. (2018) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.2018.8431516 · Cited by 2

A new rough set based bayesian classifier prior assumption

Feng, N., & Liang, Y. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-190517 · Cited by 2

A Markov Chain Load Modeling Approach through a Stream Clustering Algorithm

Massucco, S., Mosaico, G., Saviozzi, M., Silvestro, F., Fidigatti, A., & Ragaini, E. (2020) · AEIT International Annual Conference · doi:10.23919/aeit50178.2020.9241159 · Cited by 2

Book Review:The BUGS Book: A Practical Introduction to Bayesian Analysis, 1st ed., by D. Lunn, C. Jackson, N. Best, A. Thomas, and D. Spiegelhalter

Meyer, R. (2014) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2014.888324 · Cited by 2

Markov Chain Monte Carlo Models, Gibbs Sampling, & Metropolis Algorithm for High-Dimensionality Complex Stochastic ProblemsPreprint

Malhotra, Y. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2553537 · Cited by 2

On the Winning Virtuous Strategies for Ultra High Frequency Electronic Trading in Foreign Currencies Exchange MarketsPreprint

Ledenyov, D. O., & Ledenyov, V. O. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2560297 · Cited by 2

B ayesian Statistics in Quantitative Risk Assessment

Congdon, P. D. (2008) · Encyclopedia of Quantitative Risk Analysis and Assessment · doi:10.1002/9780470061596.risk0518 · Cited by 2

M arkov Chain M onte C arlo, Introduction

Ausín, M. C. (2015) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat03877.pub2 · Cited by 2

Managing the Callback Option under Arrival Rate UncertaintyPreprint

Ata, B., & Peng, X. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2947368 · Cited by 2

CLUSTERING AND CLASSIFICATION OF TIME SERIES

Peña, D., & Tsay, R. S. (2021) · Wiley series in probability and statistics · doi:10.1002/9781119417408.ch5 · Cited by 2

Markov Chain Monte Carlo

Padmanabhan, S., & Ranjan, U. (2023) · Encyclopedia of earth sciences series/Encyclopedia of earth sciences · doi:10.1007/978-3-030-85040-1_191 · Cited by 2

Ideal flow of Markov Chain

Teknomo, K. (2018) · Discrete Mathematics Algorithms and Applications · doi:10.1142/s1793830918500738 · Cited by 1

Statistical analysis and application of competing risks model with regression

Volf, P. (2019) · Croatian Operational Research Review · doi:10.17535/crorr.2019.0002 · Cited by 1

Supervised classification of spatial epidemics incorporating infection time uncertainty

Pokharel, G., Hossain, S., & Poitras, C. (2023) · Statistical Methods & Applications · doi:10.1007/s10260-023-00731-z · Cited by 1

Estimación bayesiana de un Modelo Garch-M Bivariado

Torres, C. A. C. (2024) · Revista de Matemática Teoría y Aplicaciones · doi:10.15517/rmta.v31i1.53186 · Cited by 1

Multilevel Monitoring System for Road Networks: Anomaly Detection at the Network and Road-Segment Levels

Behrooz, H., & Ilbeigi, M. (2024) · Journal of Transportation Engineering Part A Systems · doi:10.1061/jtepbs.teeng-8391 · Cited by 1

Bayesian finite mixtures of Ising models

Miao, Z., Chen, Y., & Dobra, A. (2024) · Metrika · doi:10.1007/s00184-024-00970-4 · Cited by 1

Bayesian Sampling Using Interacting Particles

Chen, S., Ding, Z., & Li, Q. (2024) · Modeling and simulation in science, engineering & technology · doi:10.1007/978-3-031-73423-6_5 · Cited by 3

Estimation of the Linear System via Optimal Transportation and Its Application for Missing Data Observations

Kang, J., Jiao, X., & Yau, S. S. ‐. (2025) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2025.3544144 · Cited by 1

Bayesian Modeling of Longitudinal Multiple-Group IRT Data with Skewed Latent Distributions and Growth Curves

dos Santos, J. R. S., Azevedo, C., & Fox, J. (2025) · Multivariate Behavioral Research · doi:10.1080/00273171.2025.2480437 · Cited by 1

Scalable Bayesian seismic wavelet estimation

Senn, G., Walker, M., & Tjelmeland, H. (2025) · Geophysical Prospecting · doi:10.1111/1365-2478.70026 · Cited by 1

Modeling healthcare resource dynamics and its application based on interregional population mobility

Guo, J., Yan, B., Yang, Y., Ning, Y., Zhou, Y. F., Zhang, R., Ma, Y., Li, J., Yu, H., & Xie, J. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1582024 · Cited by 1

A Hybrid Bayesian and Levenberg-Marquardt Approach for Estimating Boundary Heat Flow in Nonlinear Boiler Dynamics

Azevedo, J. S., Mior, L. B., Marques, M. A. F., & da Costa, M. V. A. (2025) · Optimization and Engineering · doi:10.1007/s11081-025-10011-2 · Cited by 1

Galerkin formulation of path integrals in lattice field theory

Tran, B. K., & Southworth, B. S. (2025) · Journal of Physics A Mathematical and Theoretical · doi:10.1088/1751-8121/ae1273 · Cited by 1

MeLoRA : Probability measures-based low-rank adaptation with Gaussian variational inference

He, X., Geng, X., Zhang, T., Yu, M., Zhao, Y., Yang, X., Wu, M., & Yu, G. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115318 · Cited by 1

Bayesian hierarchical models for multivariate mixed responses with repeated measures: A case study in arterial occlusive disease

Ebrahim, E. A., & Cengiz, M. A. (2026) · PLoS ONE · doi:10.1371/journal.pone.0346331 · Cited by 2

Probabilistic Prediction and Uncertainty Quantification of Bearing Capacity of Lime-Treated, Geotextile-Reinforced Silty Sand Using Bayesian Neural Network

Kumar, D., & Sultana, P. (2026) · Indian geotechnical journal · doi:10.1007/s40098-026-01529-y · Cited by 1

Comparison of Classical and Bayesian Approaches for Intervention Analysis

dos Santos, T. R., Franco, G. C., & Gamerman, D. (2010) · International Statistical Review · doi:10.1111/j.1751-5823.2010.00114.x · Cited by 1

3 Systems Biology Approaches to Understanding and Predicting Fungal Virulence

Tierney, L., Tyc, K. M., Klipp, E., & Kuchler, K. (2013) · Springer eBooks · doi:10.1007/978-3-642-39432-4_3 · Cited by 1

A note on Bayesian robustness for count data

Fúquene, J. A., & Delgado, M. (2012) · Brazilian Journal of Probability and Statistics · doi:10.1214/10-bjps134 · Cited by 1

Inference for the Hyperparameters of Structural Models Under Classical and Bayesian Perspectives: A Comparison Study

dos Santos, T. R., & Franco, G. C. (2010) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2010.509831 · Cited by 1

Capturing Latent Household Preferences in Daily-Activity Pattern Choices

Lemp, J. (2013) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2344-13 · Cited by 1

A nonparametric Bayesian approach to time series alignment

Akimoto, S., & Suematsu, N. (2010) · World Congress on Nature and Biologically Inspired Computing (NaBIC) · doi:10.1109/nabic.2010.5716376 · Cited by 1

Bayesian Pricing of the Optimal-Replication Strategy for European Option in the JD(M)J Model

Kostrzewski, M. (2012) · Dynamic Econometric Models · doi:10.12775/dem.2012.004 · Cited by 1

A Bayesian latent group analysis for detecting poor effort in a sample of cognitively impaired patients

Ortega, A., Piefke, M., & Markowitsch, H. J. (2014) · Journal of Clinical and Experimental Neuropsychology · doi:10.1080/13803395.2014.926863 · Cited by 1

Thermal Characterization of Nonhomogeneous Media

Knupp, D. C., da Fonseca, H. M., Naveira‐Cotta, C. P., Orlande, H. R. B., Cotta, R. M., & Fudym, O. (2014) · Simulation foundations, methods and applications · doi:10.1007/978-3-319-05657-9_6 · Cited by 1

Bayesian variable selection in a class of mixture models for ordinal data: a comparative study

Deldossi, L., & Paroli, R. (2014) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2014.909091 · Cited by 1

Markov Chain Monte Carlo Methods

(2015) · Chapman & Hall/CRC eBooks · doi:10.1201/b19035-19 · Cited by 1

Bayesian analysis of generalized elliptical semi-parametric models

Rondón, L. M., & Bolfarine, H. (2015) · Journal of Applied Statistics · doi:10.1080/02664763.2015.1109070 · Cited by 1

Updating Elicited Expert Judgements Using a Bayesian Framework

Profir, B., Eres, H., Scanlan, J. P., & Bates, R. (2017) · AIAA Aviation Technology, Integration, and Operations Conference (ATIO) · doi:10.2514/6.2017-3942 · Cited by 1

Thermophysical Properties Measurement and Identification

Orlande, H. R. B., & Fudym, O. (2017) · Springer eBooks · doi:10.1007/978-3-319-32003-8_5-1 · Cited by 1

slfm: An R Package to Evaluate Coherent Patterns in Microarray Data via Factor Analysis

Duarte, J., & Mayrink, V. D. (2019) · Journal of Statistical Software · doi:10.18637/jss.v090.i09 · Cited by 1

Demand Under Increasing Block Rate Pricing

Miyawaki, K. (2019) · SpringerBriefs in statistics · doi:10.1007/978-981-15-1857-7_2 · Cited by 1

Estimation of Timewise Varying Boundary Heat Flux via Bayesian Filters and Markov Chain Monte Carlo Method

da Silva, W. B., Dutra, J. C. S., Knupp, D. C., Abreu, L. A. S., & Neto, A. J. D. S. (2020) · Studies in computational intelligence · doi:10.1007/978-3-030-34409-2_8 · Cited by 1

On-the-Fly Bayesian Data Assimilation Using Transport Map Sampling and PGD Reduced Models

Rubio, P., Chamoin, L., & Louf, F. (2020) · Lecture notes in applied and computational mechanics · doi:10.1007/978-3-030-38156-1_16 · Cited by 1

Structural equation modeling with time dependence: an application comparing Brazilian energy distributors

Mayrink, V. D., Panaro, R. V., & Costa, M. A. (2020) · AStA Advances in Statistical Analysis · doi:10.1007/s10182-020-00377-2 · Cited by 1

Uncertainty Estimation for the Liquid Hydrocarbons Measurement in Static and Dynamic Measurement Systems: A Colombian Case Study

Cala, F., Nuñez, E., Bahamon, N., & Fuentes, J. A. (2021) · SPE Journal · doi:10.2118/205025-pa · Cited by 1

Multivariate State Space Models

Triantafyllopoulos, K. (2021) · Springer texts in statistics · doi:10.1007/978-3-030-76124-0_5 · Cited by 1

Frequentist Inference

Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_2 · Cited by 1

Simulation Strategies

Rossi, F. (2010) · Nanoscience and technology · doi:10.1007/978-3-642-10556-2_5 · Cited by 1

Bayesian Poisson Factorization With Side Information for User Interest Prediction in Hierarchical Edge-Caching Systems

Mehrizi, S., & Chatzinotas, S. (2022) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2022.3158811 · Cited by 1

Solving linear Bayesian inverse problems using a fractional total variation-Gaussian (FTG) prior and transport map

Sun, Z., & Zheng, G.-H. (2023) · Computational Statistics · doi:10.1007/s00180-023-01332-w · Cited by 1

A geostatistical spatially varying coefficient model for mean annual runoff that incorporates process-based simulations and short records

Roksvåg, T., Steinsland, I., & Engeland, K. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-26-5391-2022 · Cited by 1

A CVAE-within-Gibbs sampler for Bayesian linear inverse problems with hyperparameters

Yang, J., Niu, Y., & Zhou, Q. (2023) · Computational and Applied Mathematics · doi:10.1007/s40314-023-02279-w · Cited by 1

Mathematical and Statistical Approaches to the Challenge of Forecasting Animal Performance for the Purposes of Precision Livestock Feeding

Misiura, M. M., Filipe, J. A. N., & Kyriazakis, I. (2023) · Smart animal production · doi:10.1007/978-3-031-22584-0_6 · Cited by 1

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

Bohra, P., Pla, P. D. A., Giovannelli, J., & Unser, M. A. (2023) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2023.3282062 · Cited by 1

Ordinal categorical response with baseline response and skew-normal random effect: an application to schizophrenia data

Dehghani, M., & Zadkarami, M. R. (2023) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2023.2236814 · Cited by 1

Bayesian Quantile Regression Models for Complex Survey Data Under Informative Sampling

Nascimento, M. L., & Gonçalves, K. C. M. (2024) · Journal of Survey Statistics and Methodology · doi:10.1093/jssam/smae015 · Cited by 2

COMPUTATIONAL-ANALYTICAL INTEGRAL TRANSFORM AND CPU-INTENSIVE SIMULATIONS IN HEAT AND FLUID FLOW

Cotta, R. M., Abreu, L. A. S., Pontes, P. C., Naveira‐Cotta, C. P., Knupp, D. C., Orlande, H. R. B., & Colaço, M. J. (2024) · Proceeding of Second Thermal and Fluids Engineering Conference · doi:10.1615/tfec2024.kl.051359 · Cited by 1

Further evidence for diversification within the Diogenes pugilator complex (Anomura, Diogenidae) in the Mediterranean and Black Seas

Erk, C. P., Uzunova, S., Chartosia, N., Kevrekidis, K., Almón, B., & Schubart, C. D. (2024) · Crustaceana · doi:10.1163/15685403-bja10426 · Cited by 1

Bayesian Estimation of Neyman–Scott Rectangular Pulse Model Parameters in Comparison with Other Parameter Estimation Methods

Nizeyimana, P., Lee, K. E., & Kim, G. (2024) · Water · doi:10.3390/w16172515 · Cited by 1

The Effect of Congruent versus Incongruent Distractor Positioning on Electrophysiological Signals during Perceptual Decision-Making

Wongtrakun, J., Zhou, S.-H., Bellgrove, M. A., Chong, T. T. ‐., & Coxon, J. P. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.2079-23.2024 · Cited by 2

Effects of information-induced behavioral changes on brucellosis outbreak in Jilin province

Ma, X., Luo, X., & Sun, G. (2024) · International Journal of Biomathematics · doi:10.1142/s1793524524501286 · Cited by 1

Inference of time series chain graphical model

Farnoudkıa, H., & Purutçuoğlu, V. (2024) · Journal of Dynamics and Games · doi:10.3934/jdg.2024022 · Cited by 1

Parameter recovery of skew multidimensional multiple-group item response models (MGM-MIRT): a comparison of MCMC algorithms and assessment of effects of interest

Gomez, J. L. P., Azevedo, C., & Lachos, V. H. (2024) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2024.2428785 · Cited by 1

Bayesian Gravity Data Inversion Using a 3D Alpha Shape Source Representation

Abel, P. G., Fregoso, E., & Nava-Flores, M. (2025) · Pure and Applied Geophysics · doi:10.1007/s00024-024-03648-0 · Cited by 1

Ruin Probability Analysis in Automobile Insurance Using Gamma-Cubic Transmuted Exponential Distribution for Claim Severity

Hengcharoensuk, J., & Moumeesri, A. (2025) · International Journal of Mathematical Engineering and Management Sciences · doi:10.33889/ijmems.2025.10.2.024 · Cited by 1

Filling the Gaps: A Bayesian Mixture Model for Imputing Missing Soil Water Content Data

Ogle, K., Reich, E., Samuels‐Crow, K. E., Litvak, M. E., Bradford, J. B., Schlaepfer, D. R., & DeVan, M. R. (2025) · Ecohydrology · doi:10.1002/eco.70004 · Cited by 1

Road traffic estimation and distribution-based route selection

Kamphuis, R., Mandjes, M., & Serra, P. (2025) · Electronic Journal of Statistics · doi:10.1214/25-ejs2353 · Cited by 1

Seismic vulnerability model development based on Bayesian parameter estimation: Application to the South Iceland loss data

Moosapoor, M., Bessason, B., Hrafnkelsson, B., Rupakhety, R., Darzi, A., Bjarnason, J. Ö., & Erlingsson, S. (2025) · Bulletin of Earthquake Engineering · doi:10.1007/s10518-025-02145-6 · Cited by 1

Recovering knot placements in Bayesian piecewise growth models with missing data

Heo, I., Jia, F., & Depaoli, S. A. (2025) · Behavior Research Methods · doi:10.3758/s13428-025-02716-0 · Cited by 1

BayesMortalityPlus: A Package in R for Bayesian Mortality Modeling

Silva, L. M. F., Figueiredo, L. F. V., Lobo, V. G. R., Fonseca, T. C. O., & Alves, M. B. (2025) · Journal of Statistical Software · doi:10.18637/jss.v113.i09 · Cited by 1

Mean and Dispersion Regression Model for Extremes with Application in Humidity

Costa, L. R. D. S., do Nascimento, F. F., & Bourguignon, M. (2025) · Mathematics · doi:10.3390/math13182993 · Cited by 1

Efficient Implementation of Bayesian Hierarchical Model to Study Space Time Variability of Latent Heat Flux

Singh, M. K., & Venkatachalam, P. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39649-6_3 · Cited by 1

Advances in Bayesian Modeling: Applications and Methods

Yan, Y., Sosa, J. C., & Martínez, C. A. (2025) · Revista Colombiana de Estadística · doi:10.15446/rce.v48n3.122850 · Cited by 1

How accurate are Bayes factor-based null hypothesis tests? A simulation study

Schad, D. J., & Modrák, M. (2026) · Behavior Research Methods · doi:10.3758/s13428-026-03168-w · Cited by 1

Nonparametric Regression with Multiple Predictors

Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_12 · Cited by 1

Hypothesis Testing and Variable Selection

Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_4 · Cited by 1

Binary Data Models

Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_7 · Cited by 1

A Bayesian semi-parametric approach to extreme regime identification

do Nascimento, F. F., Gamerman, D., & Davis, R. A. (2016) · Brazilian Journal of Probability and Statistics · doi:10.1214/15-bjps293 · Cited by 1

Bayesian analysis of flexible measurement error models

Rondón, L. M., & Bolfarine, H. (2017) · Brazilian Journal of Probability and Statistics · doi:10.1214/16-bjps326 · Cited by 1

A Bayesian approach to wavelet-based modelling of discontinuous functions applied to inverse problems

Aykroyd, R. G., & Aljohani, H. M. (2018) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2018.1484473 · Cited by 1

Quantifying Uncertainties during the Early Design Stage of a Gas Turbine Disc by Utilizing a Bayesian Framework

Profir, B., Eres, H., Scanlan, J. P., Bates, R., & Argyrakis, C. (2018) · AIAA Aviation Technology, Integration, and Operations Conference (ATIO) · doi:10.2514/6.2018-3202 · Cited by 1

Spatially Informed Back-Calculation for Spatio-Temporal Infectious Disease Models

Pokharel, G., & Deardon, R. (2018) · Statistical Communications in Infectious Diseases · doi:10.1515/scid-2017-0001 · Cited by 1

GKFP: A New Fuzzy Clustering Method Applied to Bearings Diagnosis

Li, C., Melo, L. L. M., Delgado, M., Cerrada, M., Sánchez, R., Cabrera, D. R., & de Oliveira, J. V. (2018) · Prognostics and System Health Management Conference · doi:10.1109/phm-chongqing.2018.00227 · Cited by 1

Multivariate Bioclimatic Indices Modelling: A Coregionalised Approach

Barber, X., Conesa, D., López‐Quílez, A., & Morales, J. (2019) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1007/s13253-018-00345-z · Cited by 1

A Bayesian Approach to Infer Volcanic System Parameters, Timing, and Size of Strombolian Events From a Single Tilt Station

Manta, F., & Taisne, B. (2019) · Journal of Geophysical Research Solid Earth · doi:10.1029/2018jb016882 · Cited by 1

Estimasi Parameter Distribusi Mixture Eksponensial dan Weibull dengan Metode Bayesian Markov Chain Monte Carlo

Destiarina, U., Hadijati, M., Komalasari, D. N., & Fitriyani, N. (2019) · EIGEN MATHEMATICS JOURNAL · doi:10.29303/emj.v1i1.30 · Cited by 1

Análisis bayesiano para la diferencia de dos proporciones usando R

Rojas, H. A. G., & Zhang, H. (2016) · Revista de Métodos Cuantitativos para la Economía y la Empresa · doi:10.46661/revmetodoscuanteconempresa.2129 · Cited by 1

Vibration Based Bayesian Inference for Finite Element Model Parameters Estimation and Damage Detection

Pepi, C., & Gioffrè, M. (2020) · Lecture notes in mechanical engineering · doi:10.1007/978-3-030-41057-5_129 · Cited by 1

Inference using MCMC to a new conjugate prior to positive parameters, applied in environmental data

do Nascimento, F. F., & Moura, W. D. N. (2020) · Communications in Statistics Case Studies Data Analysis and Applications · doi:10.1080/23737484.2020.1826368 · Cited by 1

Regression models for change point data in extremes

do Nascimento, F. F., & Assunção, A. D. S. (2021) · Brazilian Journal of Probability and Statistics · doi:10.1214/20-bjps488 · Cited by 1

Estimating the efficiency of Brazilian electricity distribution utilities

Nascimento, M. G. L., Silva, R. S., de Mendonça, M. J. C., & Pereira, A. O. (2021) · Journal of Applied Statistics · doi:10.1080/02664763.2021.1890000 · Cited by 1

Twitter Network Mimicking for Data Storage Benchmarking

Speck, R., Moussallem, D., & Ngomo, A.-C. N. (2021) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc50631.2021.00057 · Cited by 2

A Bayesian Approach to Lamb-Wave Dispersion Curve Material Identification in Composite Plates

Haywood-Alexander, M., Dervilis, N., Keith Worden, B., & Rogers, T. J. (2022) · Lecture notes in civil engineering · doi:10.1007/978-3-031-07322-9_15 · Cited by 1

Discrete Event Simulation

Insua, D. R., Ruggeri, F., & Wiper, M. P. (2012) · Wiley series in probability and statistics · doi:10.1002/9780470975916.ch9 · Cited by 1

Uncertainty, Variability, and Sensitivity Analyses in Simulation Models

Isukapalli, S., Spendiff, M., Georgopoulos, P. G., & Krishnan, K. (2010) · doi:10.1002/9780470686263.ch20 · Cited by 1

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)