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

Some Characterizations of Log‐Cauchy Distribution and Its Inferential Exploration Under Progressive First‐Failure Censoring Scheme

Koley, D., Dey, S., & Yadav, A. S. (2025) · Quality and Reliability Engineering International · doi:10.1002/qre.70059 · Cited by 3

Safety Evaluation of Discontinuing Late Nighttime Flash Operations at Signalized Intersections

Lan, B., & Srinivasan, R. (2013) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2398-01 · Cited by 3

Unmixing Rasch scales: How to score an educational test

Bolsinova, M. A., Maris, G., & Hoijtink, H. J. A. (2016) · The Annals of Applied Statistics · doi:10.1214/16-aoas919 · Cited by 3

Regression models to dependence for exceedance

do Nascimento, F. F., Azevedo, A. A., & Ferraz, V. R. D. S. (2020) · Journal of Applied Statistics · doi:10.1080/02664763.2020.1795088 · Cited by 3

Inverse Population Dynamics Problem Employing a Low Cost Integral Transform Solution and Bayesian Inference with Approximation Error Model

Friguis, M. S., Knupp, D. C., Abreu, L. A. S., Stutz, L. T., & Neto, A. J. D. S. (2021) · International Journal of Applied and Computational Mathematics · doi:10.1007/s40819-021-01120-4 · Cited by 3

Determining travel fluxes in epidemic areas

Chen, D., Xue, Y., & Xiao, Y. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009473 · Cited by 3

Bayesci Yaklaşım ile Regresyon Modellerinde Parametre Tahmini

Samet, K. A. Y. A., & Babacan, E. K. (2021) · Karadeniz Fen Bilimleri Dergisi · doi:10.31466/kfbd.910703 · Cited by 3

Estimating Layers’ Structural Coefficients for Flexible Pavements in Costa Rica Road’s Network Using Full Bayesian Markov Chain Monte Carlo Approach

Elsaid, F., Amador-Jiménez, L., & Mazaheri, A. (2022) · International Journal of Pavement Research and Technology · doi:10.1007/s42947-022-00160-3 · Cited by 3

A Promotive Process of Resource Gain Against Harsh and Inconsistent Discipline in Mothers Coping With Breast Cancer: A Serial Mediation Model

Zamir, O., Bentley, G., & He, Y. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.859604 · Cited by 5

Analysis and inference of stream of dimensional errors in multistage machining process based on an improved semiparametric model

Sun, H., Zhao, S., Zhang, T., Peng, F., Zhou, L., & Yan, R. (2022) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim52237.2022.9863261 · Cited by 3

Robust parameter estimation from pulsar timing data

Samajdar, A., Shaifullah, G. M., Sesana, A., Antoniadis, J., Burgay, M., Chen, S., Cognard, I., Guillemot, L., Kramer, M., McKee, J. W., et al. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac2810 · Cited by 5

Effectiveness of Design and Implementation Alternatives for Stormwater Control Measures Modeled at the Watershed Scale

Almadani, M., Nietch, C. T., & Massoudieh, A. (2022) · Journal of Sustainable Water in the Built Environment · doi:10.1061/jswbay.sweng-460 · Cited by 3

Human Stem Cells for Ophthalmology: Recent Advances in Diagnostic Image Analysis and Computational Modelling

Wadkin, L. E., Makarenko, I., Parker, N. G., Shukurov, A., Figueiredo, F. C., & Lako, M. (2023) · Current Stem Cell Reports · doi:10.1007/s40778-023-00229-0 · Cited by 3

Airborne Pollutant Removal Effectiveness and Hidden Pollutant Source Identification of Bionic Ventilation Systems: Direct and Inverse CFD Demonstrations

Zhang, H., Li, B., Shang, J., Wang, W., & Zhao, F.-Y. (2023) · Indoor Air · doi:10.1155/2023/5522169 · Cited by 3

A comparison between marginal likelihood and data augmented MCMC algorithms for Gaussian hidden Markov models

Tancini, D., Bartolucci, F., & Pandolfi, S. (2023) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2023.2294098 · Cited by 3

Reliability assessment of offshore structures using subset simulation with adaptive standard deviation for MMH algorithm with two-stage delayed rejection

Ma, J., Lan, C., Guo, A., & Li, H. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.117040 · Cited by 3

TRANSMISSION DYNAMICS OF COVID-19 WITH DIABETES IN INDIA: A COST-EFFECTIVE AND OPTIMAL CONTROL ANALYSIS

Tripathi, J. P., Kumawat, N., Tanwar, K., PALLA, D., & Martcheva, M. (2024) · Journal of Biological Systems · doi:10.1142/s0218339024500232 · Cited by 3

Cross-modal congruency modulates evidence accumulation, not decision thresholds

Brožová, N., Vollmer, L., Kampa, B. M., Kayser, C., & Fels, J. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1513083 · Cited by 4

Pushing the boundary of child well-being: A spatial examination of child mortality in transition zones of extreme economic inequality and material hardship

Barboza-Salerno, G., Liebhard, B., Schockley-McCarthy, K., & Duhaney, S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0330449 · Cited by 3

A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters

Gerstgrasser, M., Nicholls, S., Stout, M., Smart, K. A., Powell, C. D., Kypraios, T., & Stekel, D. J. (2015) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720016500074 · Cited by 3

Numerical treatment of fiber reinforced plastic crushing mechanism in virtual performance solution: Possibilities and limitations

Hennemann, S., Hohm, V., Horst, P., & Honnakkalavar, H. (2019) · Proceedings of the Institution of Mechanical Engineers Part L Journal of Materials Design and Applications · doi:10.1177/1464420719852968 · Cited by 3

Models of making choice and control over thought for action

Indrajeet, I., Goyal, S., Miyapuram, K. P., & Ray, S. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819445-4.00004-7 · Cited by 2

A spatio-temporal model to reveal oscillator phenotypes in molecular clocks: Parameter estimation elucidates circadian gene transcription dynamics in single-cells

Unosson, M., Brancaccio, M., Hastings, M. H., Johansen, A. M., & Finkenstädt, B. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009698 · Cited by 3

Inhomogeneous evolutionary MCMC for Bayesian optimal sequential environmental monitoring

Ferreira, M. A. R. (2015) · Environmental and Ecological Statistics · doi:10.1007/s10651-015-0315-x · Cited by 3

Introduction to Bayesian Analysis of Hydrologic Variables

Fernandes, W. D. S., & Silva, A. T. (2016) · Springer eBooks · doi:10.1007/978-3-319-43561-9_11 · Cited by 3

Bayesian Contrast Measures and Clutter Distribution Determinants of Human Target Detection

Novak, A., Armstrong, N., Caelli, T., & Blair, I. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2644269 · Cited by 3

A Bayesian approach to zero-inflated data in extremes

Gramosa, A. H. Q., do Nascimento, F. F., & Morales, F. E. C. (2019) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2019.1594305 · Cited by 3

MCMC4Extremes: an R package for Bayesian inference for extremes and its extensions

Silva, W. V. M. E., & do Nascimento, F. F. (2019) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2019.1653914 · Cited by 3

Incorporating covariate information in the covariance structure of misaligned spatial data

Yarali, E., & Rivaz, F. (2020) · Environmetrics · doi:10.1002/env.2623 · Cited by 3

Flexible Effective Sample Size Based on the Message Importance Measure

Li, Z., Fan, P., & Dong, Y. (2020) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2020.3038048 · Cited by 3

A New Homotetramer Hemoglobin in the Pulmonary Surfactant of Plateau Zokors (Myospalax Baileyi)

Li, J., An, Z., Wei, L., Xu, B., Wang, Z., Gao, C., Wei, L., Qi, D., Shi, P., Zhang, T., et al. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.824049 · Cited by 3

Adversarial Variational Bayes Methods for Tweedie Compound Poisson Mixed Models

Yang, Y., Luo, R., & Liu, Y. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682184 · Cited by 3

The Impact of Markov Chain Convergence on Estimation of Mixture IRT Model Parameters

Jang, Y., & Cohen, A. S. (2020) · Educational and Psychological Measurement · doi:10.1177/0013164419898228 · Cited by 3

pexm: A JAGS Module for Applications Involving the Piecewise Exponential Distribution

Mayrink, V. D., Duarte, J., & Demarqui, F. N. (2021) · Journal of Statistical Software · doi:10.18637/jss.v100.i08 · Cited by 3

The Valuation of Long-Term Exchange Options in the German Electricity MarketPreprint

Marckhoff, J. (2009) · SSRN Electronic Journal · doi:10.2139/ssrn.1403826 · Cited by 3

Statistical inference for atmospheric transport models using process convolutions

Zhou, W., & Sansó, B. (2007) · Environmetrics · doi:10.1002/env.858 · Cited by 3

Inflation Risk Premia in the Term Structure of Interest Rates: Evidence from Euro Area Inflation SwapsPreprint

Andersen, A. S. T. (2009) · SSRN Electronic Journal · doi:10.2139/ssrn.1456831 · Cited by 3

Switching Costs and Market Power Under Umbrella BrandingPreprint

Pavlidis, P., & Ellickson, P. B. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.2147755 · Cited by 3

Markov chain models of cancer metastasisPreprint

Mason, J. M., & Newton, P. K. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/263350 · Cited by 3

The role of human intraparietal sulcus in evidence accumulation revealed by EEG and model-informed fMRIPreprint

Wongtrakun, J., Zhou, S.-H., O’Connell, R. G., Chong, T. T. ‐., Bellgrove, M. A., & Coxon, J. P. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.05.636628 · Cited by 3

Sequential Monte-Carlo sampler for Bayesian inference in complex systems

Nguyen, T. L. T. (2014) · doi:10.70675/aae2576dz60aaz4f0bzb4bfz98392197a702 · Cited by 3

Wellbore Stability Uncertainty Quantification Using a Bayesian Machine Learning Framework

Ray, P., Tipton, J., Cheng, S., & Francom, D. (2024) · SPE Annual Technical Conference and Exhibition · doi:10.2118/220771-ms · Cited by 2

Adaptive Stepsize Algorithms for Langevin Dynamics

Leroy, A., Leimkuhler, B., Latz, J., & Higham, D. J. (2024) · SIAM Journal on Scientific Computing · doi:10.1137/24m1658590 · Cited by 2

Inverse Problem of Parameter Estimation in Natural Convection of an Iron Oxide—Distilled Water Nanofluid

́on, L. A. B. V., da Silva, N. P., & Orlande, H. R. B. (2025) · Journal of Thermal Science and Engineering Applications · doi:10.1115/1.4067756 · Cited by 2

Posterior properties with censored responses using the gamma distribution

Ramos, E., Ramos, P. L., Leão, J., & Louzada, F. (2025) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2025.2479639 · Cited by 2

Advancements in Constitutive Model Calibration: Leveraging the Power of Full‐Field DIC Measurements and In Situ Load Path Selection for Reliable Parameter Inference

Ricciardi, D. E., Seidl, D. T., Lester, B., Jones, A. R., & Jones, E. M. C. (2025) · Strain · doi:10.1111/str.70008 · Cited by 2

Simulation of particle size distribution of polystyrene/expandable polystyrene in suspension polymerization with the use of modified time increment Monte Carlo model

Khajehpour‐Tadavani, S., Abedini, H., & Nekoomanesh, M. (2025) · Journal of Polymer Research · doi:10.1007/s10965-025-04536-4 · Cited by 2

Mapping Evapotranspiration and Diffuse Recharge via Variational Assimilation of GOES LST and SMAP Soil Moisture

Mahmood, A., & Farhadi, L. (2026) · Water Resources Research · doi:10.1029/2025wr040507 · Cited by 2

DeepONet-enhanced bayesian inference for biphasic tumor growth modeling

Sotiropoulos, G., Litvinov, S., Kaltenbach, S., Harkos, C., Atzarakis, K., Stylianopoulos, T., Koumoutsakos, P., & Papadopoulos, V. (2026) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2026.118919 · Cited by 2

Computational simulations of potential Pachycrocuta bite damage based on a ~1.2 Ma ravaged hippopotamus femur from Fuente Nueva 3 (Orce, Granada, Spain)

Courtenay, L. A., Serrano‐Ramos, A., Saarinen, J. J., Viranta, S., Barsky, D., Arenas, J. M. J., & Yravedra, J. (2026) · Peer Community Journal · doi:10.24072/pcjournal.771 · Cited by 2

Direct and Inverse Problems Solutions in Micro-Scale Forced Convection

Naveira‐Cotta, C. P., Cotta, R. M., Orlande, H. R. B., & Kakaç, S. (2010) · NATO science for peace and security series. A, Chemistry and biology · doi:10.1007/978-90-481-9029-4_3 · Cited by 2

Probabilistic Classifier Chain Inference via Gibbs Sampling

Li, L., Zhang, L., Guang-yi, L., & Wang, H. (2014) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2661829.2661917 · Cited by 2

Generalized multiple-point Metropolis algorithms for approximate Bayesian computation

Kobayashi, G., & Kozumi, H. (2013) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2013.836652 · Cited by 2

Healthcare-associated bloodstream infection trends under a provincial surveillance program

Fakih, I., Fortin, É., Smith, M.-A., Carignan, A., Tremblay, C. G., Villeneuve, J., Moisan, D., Frenette, C. H., Quach, C., Schmidt, A. M., et al. (2019) · Infection Control and Hospital Epidemiology · doi:10.1017/ice.2018.357 · Cited by 2

Sovereign Risk Indices and Bayesian Theory Averaging

Lenkoski, A., & Aanes, F. L. (2020) · Econometrics · doi:10.3390/econometrics8020022 · Cited by 2

Assessment of the effect of broadband expansion on the economy reviewed

de Mendonça, M. J. C., Loureiro, P. R. A., Nascimento, A. F. Z., & Ellery, R. (2021) · Review of Development Economics · doi:10.1111/rode.12780 · Cited by 2

Bayesian Hierarchical Factor Analysis for Efficient Estimation across Race/Ethnicity

Hu, J.-X., Clark, L., Shi, P., Staggs, V., Daley, C. M., & Gajewski, B. (2021) · Revista Colombiana de Estadística · doi:10.15446/rce.v44n2.87690 · Cited by 1

A Bayesian spatial voting model to characterize the legislative behavior of the Colombian Senate 2010–2014

Luque, C., & Sosa, J. C. (2022) · Journal of Applied Statistics · doi:10.1080/02664763.2022.2111678 · Cited by 2

The Spatio-temporal Epidemiology of Child Maltreatment: Using Bayesian Hierarchical Models to Assess Neighborhood Influences

Marco, M., López‐Quílez, A., Enrique Gracia, A., & Maguire‐Jack, K. (2022) · Child maltreatment · doi:10.1007/978-3-030-93096-7_2 · Cited by 2

Criterio para determinar el tamaño de muestra en procesos de simulación estocástica

Muñoz, J. D. M., & Christen, J. A. (2022) · Ingenieria y Universidad · doi:10.11144/javeriana.iued26.cdss · Cited by 2

Probability-Based Approach for Inverse Stochastic Structural Problems

Laudani, R., & Falsone, G. (2023) · International Journal of Applied Mechanics · doi:10.1142/s1758825123500412 · Cited by 2

First report of Planomicrobium okeanokoites associated with Himantothallus grandifolius (Desmarestiales, Phaeophyta) from Southern Hemisphere

Saini, K. C., Gupta, K., Sharma, S., Gautam, A. K., Shamim, S., Mittal, D., Kundu, P., & Bast, F. (2023) · PLoS ONE · doi:10.1371/journal.pone.0282516 · Cited by 2

Yield Curve Models with Regime Changes: An Analysis for the Brazilian Interest Rate Market

Tavanielli, R., & Laurini, M. P. (2023) · Mathematics · doi:10.3390/math11112549 · Cited by 2

Estimating the number of atmospheric releases and other parameters by Bayesian inference

Albani, R. A. D. S., Albani, V., Gomes, L. E. S., Migon, H. S., & Neto, A. J. D. S. (2024) · Air Quality Atmosphere & Health · doi:10.1007/s11869-023-01497-9 · Cited by 2

Unsupervised Bayesian change point detection model to track acute stress responses

Shishavan, H. H., Gossett, E. W., Bi, J., Henning, R. A., Cherniack, M. C., & Kim, I.-S. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106415 · Cited by 3

Application of a Bayesian-Based Integrated Approach for Groundwater Contamination Sources Parameter Identification Considering Observation Error

Yan, X., & An, Y. (2024) · Water · doi:10.3390/w16111618 · Cited by 2

Bayesian analysis for social science research

Luque, C., & Sosa, J. C. (2024) · Model Assisted Statistics and Applications · doi:10.3233/mas-231460 · Cited by 2

Contribution of the diatom Navicula sp. to the growth of Penaeus vannamei post-larvae in biofloc system: a quantitative stable isotope assessment

dos Santos, E. P., de Abreu, J. L., Dantas, Y. D. M., Oliveira, C. Y. B., Costa, G. A., Yogui, G. T., Andrade, H. A., Brito, L. O., & Gálvez, A. O. (2024) · Isotopes in Environmental and Health Studies · doi:10.1080/10256016.2024.2397472 · Cited by 2

Online variational Gaussian process for time series data

Wang, W., Khalil, M. M. Y., & Bayisa, L. Y. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-01005-5 · Cited by 2

Analysis of the SEIR mean-field model in dynamic networks under intervention

Li, J., Jin, Z., & Tang, M. (2025) · Infectious Disease Modelling · doi:10.1016/j.idm.2025.03.002 · Cited by 2

Diagnostic potential of monocyte subsets, TNF-α, and IL-6 in pediatric celiac disease: A case-control study

Farag, N. M., ElBakry, N. M., Mousa, M. M., Hemeda, M. S., & Mohamed, Z. H. (2025) · Aspects of Molecular Medicine · doi:10.1016/j.amolm.2025.100083 · Cited by 2

Data-driven Radiative Hydrodynamics Simulations of the Solar Photosphere Using Physics-informed Neural Networks: Proof of Concept

Keller, C. U. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/add6a9 · Cited by 3

Bayesian Bell Regression Model for Fitting of Overdispersed Count Data with Application

Alhseeni, A. M. I., & Bevrani, H. (2025) · Stats · doi:10.3390/stats8040095 · Cited by 2

Photometric Analysis of 30 Contact Binaries in M31

Gao, X., Li, K., Wang, L.-H., Yuan, H., Gu, H., & Guo, Y. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/ae11a3 · Cited by 2

Predictive Modeling of Threshold Life Tables

Ji, M., Aminzadeh, M. S., & Deng, M. (2019) · North American Actuarial Journal · doi:10.1080/10920277.2019.1633929 · Cited by 2

A non–stationary non–Gaussian hedonic spatial model for house selling prices

Oliveira, V. D., & Ecker, M. D. (2019) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2019.1704418 · Cited by 2

Application of Stochastic Optimization to Optimal Preventive Maintenance Problem

Volf, P. (2021) · Journal of Applied Mathematics and Physics · doi:10.4236/jamp.2021.910157 · Cited by 2

Introduction and Motivating Examples

Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_1 · Cited by 2

Estimation of Item and Person Parameters

Reckase, M. D. (2009) · Springer eBooks · doi:10.1007/978-0-387-89976-3_6 · Cited by 2

Stochastic models for greenhouse gas emission rate estimation from hydroelectric reservoirs: a Bayesian hierarchical approach

Israel, V. P., & Migon, H. S. (2012) · Journal of Applied Statistics · doi:10.1080/02664763.2011.636417 · Cited by 2

Estimating the Hatchery Fraction of a Natural Population: A Bayesian Approach

Barber, J. J., Gerow, K., Connolly, P. J., & Singh, S. (2011) · North American Journal of Fisheries Management · doi:10.1080/02755947.2011.633687 · Cited by 2

Do maternal health problems influence child's worrying status? Evidence from the British Cohort Study

Dai, X., Härdle, W. K., & Yu, K. (2016) · Journal of Applied Statistics · doi:10.1080/02664763.2016.1155203 · Cited by 2

A Bayesian model for predicting local El Niño events using tree ring widths and cellulose δ18O

Nippert, J. B., Hooten, M. B., Sandquist, D. R., & Ward, J. (2010) · Journal of Geophysical Research Atmospheres · doi:10.1029/2009jg001101 · Cited by 2

Risk Analysis of Structural Engineering Systems Using Bayesian Inference

Faroz, S. A., Pujari, N. N., Rastogi, R., & Ghosh, S. (2016) · Advances in civil and industrial engineering book series · doi:10.4018/978-1-5225-0588-4.ch014 · Cited by 2

A clustering cure rate model with application to a sealant study

Gallardo, D. I., Bolfarine, H., & Pedroso-de-Lima, A. C. (2016) · Journal of Applied Statistics · doi:10.1080/02664763.2016.1267116 · Cited by 2

Toward Bayesian inference of the spatial distribution of proteins from three-cube Förster resonance energy transfer data

Hooghoudt, J.-O., Barroso, M., & Waagepetersen, R. P. (2017) · The Annals of Applied Statistics · doi:10.1214/17-aoas1054 · Cited by 2

Spatially adaptive Bayesian image reconstruction through locally-modulated Markov random field models

Gezeri, S. M. A., & Aykroyd, R. G. (2019) · Brazilian Journal of Probability and Statistics · doi:10.1214/18-bjps399 · Cited by 2

A change-point approach for the identification of financial extreme regimes

Lattanzi, C., & Leonelli, M. (2021) · Brazilian Journal of Probability and Statistics · doi:10.1214/21-bjps509 · Cited by 2

Rate of ozone and PM10exceedances: A case study comparing data from Mexico City and Bogota

Suárez-Sierra, B. M., Rodrigues, E. R., & Tzintzun, G. (2019) · Communications in Statistics Case Studies Data Analysis and Applications · doi:10.1080/23737484.2019.1593066 · Cited by 2

A Bayesian Nonlinear Regression Model Based on t-Distributed Errors

Dorndorf, A., Kargoll, B., Paffenholz, J., & Alkhatib, H. (2019) · International Association of Geodesy symposia · doi:10.1007/1345_2019_76 · Cited by 2

Comparison of Count Modeling Techniques for Estimating Environmental Monitoring Limits in Clean Rooms

Santos, P. A. D. L., Kim, J. Y., IJzerman‐Boon, P. C., Kariuki, G. G., & Smith, B. M. (2020) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2020.1799854 · Cited by 2

Bayesian mixture cure rate frailty models with an application to gastric cancer data

Karamoozian, A., Baneshi, M. R., & Bahrampour, A. (2020) · Statistical Methods in Medical Research · doi:10.1177/0962280220974699 · Cited by 2

Non-Linear and Non-Gaussian State Space Models

Triantafyllopoulos, K. (2021) · Springer texts in statistics · doi:10.1007/978-3-030-76124-0_6 · Cited by 2

Goodness of Fit and Parameter Uncertainty

Bonamente, M. (2016) · Graduate texts in physics · doi:10.1007/978-1-4939-6572-4_10 · Cited by 2

Thermophysical Properties Measurement and Identification

Orlande, H. R. B., & Fudym, O. (2018) · Springer eBooks · doi:10.1007/978-3-319-26695-4_5 · Cited by 2

Synergy of stochastics and inelasticity at multiple scales: novel Bayesian applications in stochastic upscaling and fracture size and scale effects

Ibrahimbegović, A., Matthies, H. G., Dobrilla, S., Karavelić, E., Nava, R. A. M., Nguyen, C. U., Sarfaraz, M. S., Stanić, A., & Vondřejc, J. (2022) · SN Applied Sciences · doi:10.1007/s42452-022-04935-y · Cited by 2

A dicotomia do trabalho docente frente à virtualização de emergência na educação no contexto da pandemia da Covid-19

Marques, R., Lelis, D. A. D. J., & Gonzalez, C. E. F. (2022) · Research Society and Development · doi:10.33448/rsd-v11i9.32163 · Cited by 2

Modeling and Hybrid Inversion of Mineral Deposits Using the Dipping Dike Model with Finite Depth Extent

Oliveira, S. P., Azevedo, J. S., Batista, J. D. C., & Novais, D. M. (2024) · Minerals · doi:10.3390/min14101054 · Cited by 2

Using INLA to Estimate a Highly Dimensional Spatial Model for Forest Fires in Portugal

Natário, I., Oliveira, M., & Marques, S. (2014) · Studies in theoretical and applied statistics. Selected papers of the statistical societies · doi:10.1007/978-3-319-05323-3_23 · Cited by 2

Class of Exponentiated Distributions Introduction

AL-Hussaini, E. K., & Ahsanullah, M. (2015) · Atlantis studies in probability and statistics · doi:10.2991/978-94-6239-079-9_1 · Cited by 2

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)