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

On the mechanistic understanding of predator feeding behavior using the functional response concept

Papanikolaou, N. E., Broufas, G. D., Papachristos, D., Pappas, M. L., Kyriakaki, C., Σαμαράς, Κ., & Kypraios, T. (2020) · Ecosphere · doi:10.1002/ecs2.3147 · Cited by 13

Effects of Prenatal and Postbirth Intimate Partner Violence on Preschool-Age Children’s Dissociative Symptoms

Yalch, M., Black, J. A., Martin, L. F., & Levendosky, A. (2016) · Journal of Aggression Maltreatment & Trauma · doi:10.1080/10926771.2016.1194937 · Cited by 12

Quantification of Uncertainty in Creep Failure of RF-MEMS Switches

Kolis, P., Bajaj, A. K., & Koslowski, M. (2016) · Journal of Microelectromechanical Systems · doi:10.1109/jmems.2016.2636841 · Cited by 13

Estimation of annual runoff by exploiting long-term spatial patterns and short records within a geostatistical framework

Roksvåg, T., Steinsland, I., & Engeland, K. (2020) · Hydrology and earth system sciences · doi:10.5194/hess-24-4109-2020 · Cited by 12

Use of Bayesian Methods in the Process of Uranium Bioleaching by Acidithiobacillus ferrooxidans

Cardoso, A. C., Dias, C. S., de Moura, C. H. R., Ferreira, J. L., Rodrigues, E. C., Macêdo, E. N., Estumano, D. C., & Viegas, B. M. (2023) · Applied Sciences · doi:10.3390/app14010109 · Cited by 12

GLAMbox: A Python toolbox for investigating the association between gaze allocation and decision behaviour

Molter, F., Thomas, A. W., Heekeren, H. R., & Mohr, P. N. C. (2019) · PLoS ONE · doi:10.1371/journal.pone.0226428 · Cited by 13

Latent Space Modeling of Hypergraph Data

Turnbull, K., Lunagómez, S., Nemeth, C., & Airoldi, E. M. (2023) · Journal of the American Statistical Association · doi:10.1080/01621459.2023.2270750 · Cited by 12

A Cortical Surface-Based Meta-Analysis of Human Reasoning

Shin, M., & Jeon, H. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab174 · Cited by 13

Empirical Bayesian Inference Using a Support Informed Prior

Zhang, J., Gelb, A., & Scarnati, T. (2022) · SIAM/ASA Journal on Uncertainty Quantification · doi:10.1137/21m140794x · Cited by 12

Taming model uncertainty in self-adaptive systems using bayesian model averaging

Camilli, M., Mirandola, R., & Scandurra, P. (2022) · International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) · doi:10.1145/3524844.3528056 · Cited by 12

Multipole expansion for 21 cm intensity mapping power spectrum: Forecasted cosmological parameters estimation for the SKA observatory

Berti, M., Spinelli, M., & Viel, M. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad685 · Cited by 12

Parameter recovery for a skew-normal IRT model under a Bayesian approach: hierarchical framework, prior and kernel sensitivity and sample size

Azevedo, C., Bolfarine, H., & de Andrade, D. F. (2011) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2011.591798 · Cited by 12

Comparison study between MCMC-based and weight-based Bayesian methods for identification of joint distribution

Noh, Y., Choi, K. K., & Lee, I. (2010) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-010-0539-1 · Cited by 12

Application of the Bayesian Inference for Estimation of the Long-Term Noise Indicators and Their Uncertainty

Batko, W., & Stępień, B. (2011) · Acta Physica Polonica A · doi:10.12693/aphyspola.119.916 · Cited by 12

Relaxed Poisson cure rate models

Rodrigues, J., Cordeiro, G. M., Cancho, V. G., & Balakrishnan, N. (2015) · Biometrical Journal · doi:10.1002/bimj.201500051 · Cited by 12

A Novel Hybrid-Jump-Based Sampling Method for Complex Social Networks

Liu, L., Wang, L., Wu, W., Jia, H., & Zhang, Y. (2019) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2019.2893889 · Cited by 12

Machine Learning Approach to Improve Accuracy of Warpage Simulations

Selvanayagam, C., Duong, P. L. T., Mandal, R., & Raghavan, N. (2019) · IEEE Electronic Components and Technology Conference (ECTC) · doi:10.1109/ectc.2019.00131 · Cited by 12

AI-Assisted Package Design for Improved Warpage Control of Ultra-Thin Packages

Selvanayagam, C., Duong, P. L. T., & Raghavan, N. (2020) · International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) · doi:10.1109/eurosime48426.2020.9152714 · Cited by 12

Developmental Sex Differences in Negative Emotion Decision-Making Dynamics: Computational Evidence and Amygdala-Prefrontal Pathways

Xu, J., Lei, H. C., Chen, M., He, Y., Jiang, M., Tian, T., Wang, H., Wang, Y., Wang, D., Han, Z. R., et al. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab359 · Cited by 11

Updated knowledge in the estimation of genetics parameters: a Bayesian approach in white oat (Avena sativa L.)

Azevedo, C. F., Nascimento, M., Carvalho, I. R., Nascimento, A. C. C., de Almeida, H. C. F., Cruz, C. D., & da Silva, J. A. G. (2022) · Euphytica · doi:10.1007/s10681-022-02995-0 · Cited by 12

Markov Chain Monte Carlo

Johannes, M. S., & Polson, N. (2009) · Springer eBooks · doi:10.1007/978-3-540-71297-8_43 · Cited by 12

Impact of Machine Reliability Data Uncertainty on the Design and Operation of Manufacturing Systems

Colledani, M., & Yemane, A. (2013) · Procedia CIRP · doi:10.1016/j.procir.2013.06.032 · Cited by 12

Spatially varying temperature trends in a Central California Estuary

Lemos, R. T., Sansó, B., & Huertos, M. L. (2007) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1198/108571107x227603 · Cited by 12

A non-homogeneous poisson model with spatial anisotropy applied to ozone data from Mexico City

Rodrigues, E. R., Gamerman, D., Tarumoto, M. H., & Tzintzun, G. (2014) · Environmental and Ecological Statistics · doi:10.1007/s10651-014-0303-6 · Cited by 12

Voting Advice Applications: Missing Value Estimation Using Matrix Factorization and Collaborative Filtering

Agathokleous, M., & Tsapatsoulis, N. (2013) · IFIP advances in information and communication technology · doi:10.1007/978-3-642-41142-7_3 · Cited by 12

BAYESIAN CALIBRATION WITH ADAPTIVE MODEL DISCREPANCY

Leoni, N., Maître, O. P. L., Rodio, M. G., & Congedo, P. M. (2023) · International Journal for Uncertainty Quantification · doi:10.1615/int.j.uncertaintyquantification.2023046331 · Cited by 11

COSINE-100 full dataset challenges the annual modulation signal of DAMA/LIBRA

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., Jeon, E. J., et al. (2025) · Science Advances · doi:10.1126/sciadv.adv6503 · Cited by 11

An optimization for reducing the size of an existing urban-like monitoring network for retrieving an unknown point source emission

Kouichi, H., Ngae, P., Kumar, P., Feiz, A. A., & Bekka, N. (2019) · Geoscientific model development · doi:10.5194/gmd-12-3687-2019 · Cited by 11

Real-time data assimilation and control on mechanical systems under uncertainties

Rubio, P., Chamoin, L., & Louf, F. (2021) · Advanced Modeling and Simulation in Engineering Sciences · doi:10.1186/s40323-021-00188-3 · Cited by 11

The critical role of interference control in metaphor comprehension evidenced by the drift–diffusion model

Yoon, H.-D., Shin, M., & Jeon, H. (2021) · Scientific Reports · doi:10.1038/s41598-021-98351-8 · Cited by 10

Hierarchical multivariate directed acyclic graph autoregressive models for spatial diseases mapping

Gao, L., Datta, A., & Banerjee, S. (2022) · Statistics in Medicine · doi:10.1002/sim.9404 · Cited by 11

How spatial attention affects the decision process: looking through the lens of Bayesian hierarchical diffusion model & EEG analysis

Ghaderi-Kangavari, A., Parand, K., Ebrahimpour, R., Nunez, M. D., & Rad, J. A. (2023) · Journal of Cognitive Psychology · doi:10.1080/20445911.2023.2187714 · Cited by 13

Measuring the vulnerability of the Uruguayan population to vector-borne diseases via spatially hierarchical factor models

Lopes, H. F., Schmidt, A. M., Salazar, E., Gómez, M., & Achkar, M. (2012) · The Annals of Applied Statistics · doi:10.1214/11-aoas497 · Cited by 11

Nonstandard Personalized Medicine Strategies for Cancer May Lead to Improved Patient Outcomes

Beckman, R. A., & Yeang, C. (2014) · Personalized Medicine · doi:10.2217/pme.14.57 · Cited by 11

A Hierarchical Model for Aggregated Functional Data

Dias, R., Garcia, N. L., & Schmidt, A. M. (2013) · Technometrics · doi:10.1080/00401706.2013.765316 · Cited by 10

Bayesian Approach to Personalized Benefit-Risk Assessment

Cui, S., Zhao, Y., & Tiwari, R. C. (2016) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2016.1193045 · Cited by 11

Structural damage identification via time domain response and Markov Chain Monte Carlo method

Teixeira, J. D. S., Stutz, L. T., Knupp, D. C., & Neto, A. J. D. S. (2016) · Inverse Problems in Science and Engineering · doi:10.1080/17415977.2016.1209749 · Cited by 11

Dynamic Spatial Models Including Spatial Time Series

Gamerman, D. (2010) · Chapman & Hall/CRC interdisciplinary statistics series/Interdisciplinary statistics · doi:10.1201/9781420072884-c24 · Cited by 11

Regression models for time-varying extremes

Lima, S. R., do Nascimento, F. F., & Ferraz, V. R. D. S. (2017) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2017.1385788 · Cited by 11

The Impact of Forecasting Jumps on Forecasting Electricity Prices

Kostrzewski, M., & Kostrzewska, J. (2021) · Energies · doi:10.3390/en14020336 · Cited by 12

A novel energy-based online sequential extreme learning machine to detect anomalies over real-time data streams

Wang, X., Tu, S., Zhao, W., & Shi, C. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05731-2 · Cited by 11

Climate Change Induced Trends and Uncertainties in Phytoplankton Spring Bloom Dynamics

Mészáros, L., van der Meulen, F., Jongbloed, G., & Serafy, G. Y. E. (2021) · Frontiers in Marine Science · doi:10.3389/fmars.2021.669951 · Cited by 11

Spatial Difference Boundary Detection for Multiple Outcomes Using Bayesian Disease Mapping

Gao, L., Banerjee, S., & Ritz, B. R. (2022) · Biostatistics · doi:10.1093/biostatistics/kxac013 · Cited by 11

Bayesian Extreme for Modeling High PM10 Concentration in Johor

Amin, N. A. M., Adam, M. B., & Aris, A. Z. (2015) · Procedia Environmental Sciences · doi:10.1016/j.proenv.2015.10.055 · Cited by 11

Modern Bayesian Factor Analysis

Lopes, H. F. (2014) · doi:10.1002/9781118771051.ch5 · Cited by 11

Conditional Autoregressive ( CAR ) Model

Schmidt, A. M., & Nobre, W. S. (2018) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat08048 · Cited by 11

Clayton copula for survival data with dependent censoring: An application to a tuberculosis treatment adherence data

Schneider, S. I., dos Reis, R. C. P., Gottselig, M. M. F., Fisch, P., Knauth, D. R., & Vigo, Á. (2023) · Statistics in Medicine · doi:10.1002/sim.9858 · Cited by 10

An introduction to bayesian spatial smoothing methods for disease mapping: modeling county firearm suicide mortality rates

Gause, E. L., Schumacher, A. E., Ellyson, A. M., Withers, S. D., Mayer, J. D., & Rowhani‐Rahbar, A. (2024) · American Journal of Epidemiology · doi:10.1093/aje/kwae005 · Cited by 11

Trends and Variability of Temperatures in the Eastern Province of Rwanda

Rwema, M., Safari, B., Laine, M., Sylla, M. B., & Roininen, L. (2025) · International Journal of Climatology · doi:10.1002/joc.8793 · Cited by 10

Real-time modelling of a pandemic influenza outbreak

Birrell, P., Pebody, R. G., Charlett, A., Zhang, X., & Angelis, D. D. (2017) · Health Technology Assessment · doi:10.3310/hta21580 · Cited by 10

A novel approach for subsurface characterization of coupled fluid flow and geomechanical deformation: the case of slightly compressible flows

Borges, M. R., & Pereira, F. (2020) · Computational Geosciences · doi:10.1007/s10596-020-09980-3 · Cited by 10

Modeling and Detection of Future Cyber-Enabled DSM Data Attacks

Hatalis, K., Zhao, C., Venkitasubramaniam, P., Snyder, L., Kishore, S., & Blum, R. S. (2020) · Energies · doi:10.3390/en13174331 · Cited by 10

Bayesian additive regression trees for genotype by environment interaction models

Sarti, D. A., Prado, E. B., Inglis, A., dos Santos, A. A. L., Hurley, C., Moral, R. D. A., & Parnell, A. C. (2023) · The Annals of Applied Statistics · doi:10.1214/22-aoas1698 · Cited by 10

Bayesian Nonmetric Successive Categories Multidimensional Scaling

Okada, K., & Mayekawa, S.-I. (2011) · Behaviormetrika · doi:10.2333/bhmk.38.17 · Cited by 10

Bayes and Empirical Bayes Methods for Reduced Rank Regression Models in Matched Case-Control Studies

Satagopan, J. M., Sen, A., Zhou, Q. C., Qing Lan, B., Rothman, N., Langseth, H., & Engel, L. S. (2015) · Biometrics · doi:10.1111/biom.12444 · Cited by 10

A parametric model for seismic wavelets—with estimation and uncertainty quantification

Skauvold, J., Eidsvik, J., & Theune, U. (2016) · Geophysical Journal International · doi:10.1093/gji/ggw029 · Cited by 10

Warming Trends and Long-Range Dependent Climate Variability Since Year 1900: A Bayesian Approach

Myrvoll-Nilsen, E., Fredriksen, H., Sørbye, S. H., & Rypdal, M. W. (2019) · Frontiers in Earth Science · doi:10.3389/feart.2019.00214 · Cited by 12

Analysis of two Weibull populations under joint progressively hybrid censoring

Abo-Kasem, O. E., & Elshahhat, A. (2021) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2021.1963452 · Cited by 10

Bayesian Multistate Life Table Methods for Large and Complex State Spaces: Development and Illustration of a New Method

Lynch, S. M., & Zang, E. X. (2022) · Sociological Methodology · doi:10.1177/00811750221112398 · Cited by 10

Sensitivity of aerosol optical depth trends using long-term measurements of different sun photometers

Karanikolas, A., Kouremeti, N., Gröbner, J., Egli, L., & Kazadzis, S. (2022) · Atmospheric measurement techniques · doi:10.5194/amt-15-5667-2022 · Cited by 10

Generalized graded unfolding model with structural equation for subject parameters

Usami, S. (2011) · Japanese Psychological Research · doi:10.1111/j.1468-5884.2011.00476.x · Cited by 10

Estimating pipeline location using ground-penetrating radar data in the presence of model uncertainties

Lähivaara, T., Ward, N. F. D., Huttunen, T., Kaipio, J. P., & Niinimäki, K. (2014) · Inverse Problems · doi:10.1088/0266-5611/30/11/114006 · Cited by 10

Bayes Model Selection with Path Sampling: Factor Models and Other Examples

Dutta, R., & Ghosh, J. K. (2013) · Statistical Science · doi:10.1214/12-sts403 · Cited by 10

A prefetching technique for prediction of porous media flows

Ginting, V., Pereira, F., & Rahunanthan, A. (2014) · Computational Geosciences · doi:10.1007/s10596-014-9413-3 · Cited by 9

A statistical inversion approach for local thermal diffusivity and heat flux simultaneous estimation

da Fonseca, H. M., Orlande, H. R. B., Fudym, O., & Sepúlveda, F. (2014) · Quantitative InfraRed Thermography Journal · doi:10.1080/17686733.2014.947860 · Cited by 10

Estimating Network Kinetics of the MAPK/ERK Pathway Using Biochemical Data

Purutçuoğlu, V., & Wit, E. C. (2012) · Mathematical Problems in Engineering · doi:10.1155/2012/752631 · Cited by 10

Maximum likelihood versus likelihood-free quantum system identification in the atom maser

Catana, C., Kypraios, T., & Guţǎ, M. (2014) · Journal of Physics A Mathematical and Theoretical · doi:10.1088/1751-8113/47/41/415302 · Cited by 10

Particle Learning for Fat-Tailed Distributions

Lopes, H. F., & Polson, N. G. (2015) · Econometric Reviews · doi:10.1080/07474938.2015.1092809 · Cited by 10

Geostatistical mixed beta regression: a Bayesian approach

Lagos-Álvarez, B. M., Fustos-Toribio, R., Figueroa-Zúñiga, J., & Mateu, J. (2016) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-016-1308-5 · Cited by 10

A Two-Field Geostatistical Model Combining Point and Areal Observations—A Case Study of Annual Runoff Predictions in the Voss Area

Roksvåg, T., Steinsland, I., & Engeland, K. (2021) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/rssc.12492 · Cited by 10

Point pattern analysis with spatially varying covariate effects, applied to the study of cerebrovascular deaths

Pinto, J. A., Gamerman, D., Paez, M. S., & Alves, R. H. F. (2014) · Statistics in Medicine · doi:10.1002/sim.6389 · Cited by 10

Habit, Long-Run Risks, Prospect? A Statistical InquiryPreprint

Aldrich, E. M., & Gallant, A. R. (2010) · SSRN Electronic Journal · doi:10.2139/ssrn.1674030 · Cited by 10

Predictive Distributions for Constructing the ICH Q8 Design Space

Peterson, J. J., Yahyah, M., Lief, K., & Hodnett, N. S. (2017) · doi:10.1002/9781119356189.ch4 · Cited by 10

Investigating spatial variability in COVID-19 pandemic severity across 19 geographic areas, Spain, 2020Preprint

Dahal, S., Mizumoto, K., ROTHENBERG, R. B., & Chowell, G. (2020) · medRxiv · doi:10.1101/2020.04.14.20065524 · Cited by 10

A Bayes Factor for Replications of ANOVA ResultsPreprint

Harms, C. (2018) · OSF Preprints (OSF Preprints) · Cited by 9

Bayesian analysis of the p-order integer-valued AR process with zero-inflated Poisson innovations

Garay, A. M., Medina, F. L., Cabral, C. R. B., & Lin, T. (2020) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2020.1754819 · Cited by 9

Impact of flow geometry on parameter uncertainties for underdamped slug tests in fractured rocks

Dausse, A., Guihéneuf, N., & Parker, B. L. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.125567 · Cited by 9

Simple Method for Incorporating Prior Parameter Information during Model-Based D-Optimal Design of Experiments for Multi-Response Chemical Process Models

Gibson, L. A., Liu, F. F., Harris, T. J., & McAuley, K. B. (2024) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.3c04058 · Cited by 9

Emulation‐based inference for spatial infectious disease transmission models incorporating event time uncertainty

Pokharel, G., & Deardon, R. (2021) · Scandinavian Journal of Statistics · doi:10.1111/sjos.12523 · Cited by 9

Inverse Design for Low Warpage Ultra-Thin Packages Using Constrained Particle Swarm Optimization

Selvanayagam, C., Duong, P. L. T., Wilkerson, B., & Raghavan, N. (2021) · IEEE Access · doi:10.1109/access.2021.3072128 · Cited by 9

Artificial Neural Networks and Bayesian Techniques for Flip-Chip Package Thermo-Mechanical Analysis

Sinha, T., Sikka, K. K., & Lall, R. K. (2021) · IEEE Electronic Components and Technology Conference (ECTC) · doi:10.1109/ectc32696.2021.00230 · Cited by 9

Surface Transfer Coefficients Estimation for Heat Conduction Problem Using the Bayesian Framework

Berger, J., & Legros, C. (2022) · Heat Transfer Engineering · doi:10.1080/01457632.2022.2068217 · Cited by 9

Exploring the Relationship among Human Activities, COVID-19 Morbidity, and At-Risk Areas Using Location-Based Social Media Data: Knowledge about the Early Pandemic Stage in Wuhan

Yuan, M., Liu, T., & Yang, C. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19116523 · Cited by 9

Analysis of Load-Altering Attacks Against Power Grids: A Rare-Event Sampling Approach

Goodridge, M. P., Lakshminarayana, S., & Few, C. (2022) · International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) · doi:10.1109/pmaps53380.2022.9810602 · Cited by 9

Unlocking ensemble ecosystem modelling for large and complex networks

Vollert, S. A., Drovandi, C., & Adams, M. P. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011976 · Cited by 9

Hierarchical Bayesian modelling of wind and sea surface temperature from the Portuguese coast

Lemos, R. T., Sansó, B., & Santos, F. D. (2009) · International Journal of Climatology · doi:10.1002/joc.1981 · Cited by 9

Model Choice Using the Deviance Information Criterion for Latent Conditional Individual-Level Models of Infectious Disease Spread

Deeth, L. E., Deardon, R., & Gillis, D. J. (2015) · Epidemiologic Methods · doi:10.1515/em-2014-0001 · Cited by 9

Derivation of a real-life driving cycle from fleet testing data with the Markov-Chain-Monte-Carlo Method

Fries, M., Baum, A., Wittmann, M., & Lienkamp, M. (2018) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2018.8569547 · Cited by 9

Impact of Damping Models in Damage Identification

de Souza, M. L. M., Castello, D. A., Roitman, N., & Ritto, T. G. (2019) · Shock and Vibration · doi:10.1155/2019/4652328 · Cited by 9

Learning Localized Spatial Material Properties of Substrates in Ultra-Thin Packages Using Markov Chain Monte Carlo and Finite Element Analysis

Selvanayagam, C., Duong, P. L. T., & Raghavan, N. (2020) · IEEE Access · doi:10.1109/access.2020.2980039 · Cited by 9

The effect of childhood unpredictability on co-parenting relationships during the transition to parenthood: A life history approach

Szepsenwol, O. (2020) · Journal of Social and Personal Relationships · doi:10.1177/0265407520918670 · Cited by 7

Analysis of the age-structured epidemiological characteristics of SARS-COV-2 transmission in mainland China: An aggregated approach

Yang, J., Wang, G., Zhang, S., Xu, F., & Li, X.-Z. (2020) · Mathematical Modelling of Natural Phenomena · doi:10.1051/mmnp/2020032 · Cited by 9

Bayesian adaptive nearest neighbor

Guo, R., & Chakraborty, S. (2010) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.10067 · Cited by 9

Monte Carlo parameter estimation and direct simulation of in vitro hyperthermia-chemotherapy experiment

da Silva, N. P., Varón, L. A. B., da Costa, J. M. J., & Orlande, H. R. B. (2021) · Numerical Heat Transfer Part A Applications · doi:10.1080/10407782.2021.1940009 · Cited by 9

Bayesian MCMC Estimation

Mills, J. A., & Parent, O. (2013) · Springer eBooks · doi:10.1007/978-3-642-23430-9_89 · Cited by 9

Two-stage hybrid model for hydrological series prediction based on a new method of partitioning datasets

Xu, H., Song, S., Guo, T., & Wang, H. (2022) · Journal of Hydrology · doi:10.1016/j.jhydrol.2022.128122 · Cited by 9

A Mathematical Model for Ovine Brucellosis during Dynamic Transportation of Sheep, and Its Applications in Jalaid Banner and Ulanhot City

Guo, J., Luo, X., Zhang, J., & Li, M. (2022) · Mathematics · doi:10.3390/math10193436 · Cited by 9

A Generalized Linear Mixed Model for Data Breaches and Its Application in Cyber Insurance

Sun, M., & Lu, Y. H. (2022) · Risks · doi:10.3390/risks10120224 · Cited by 9

The Effect of Late Onset, Short-Term Caloric Restriction on the Core Temperature and Physical Activity in Mice

Golightly, A., Boys, R. J., Cameron, K., & von Zglinicki, T. (2012) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/j.1467-9876.2012.01045.x · Cited by 9

Modeling quasi-periodic signals by a non-parametric model: Application on fetal ECG extraction

Noorzadeh, S., Niknazar, M., Rivet, B., Fontecave-Jallon, J., Guméry, P.-Y., & Christian Jutten, B. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6943979 · Cited by 9

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)