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

Observation-based blended projections from ensembles of regional climate models

Salazar, E., Hammerling, D., Wang, X., Sansó, B., Finley, A. O., & Mearns, L. O. (2016) · Climatic Change · doi:10.1007/s10584-016-1722-1 · Cited by 5

Bayesian parameter estimation using truncated normal distributions as priors for parameters in fundamental models of chemical processes

Gibson, L. A., & McAuley, K. B. (2024) · The Canadian Journal of Chemical Engineering · doi:10.1002/cjce.25398 · Cited by 5

Uncertainty-aware waveform modeling for high signal-to-noise ratio gravitational-wave inference

Mezzasoma, S., Haster, C. ‐., Owen, C. B., Cornish, N. J., & Yunes, N. (2025) · Physical review. D/Physical review. D. · doi:10.1103/hfq5-gryy · Cited by 7

Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs

Tolpin, D., van de Meent, J.-W., Paige, B., & Wood, F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23525-7_19 · Cited by 5

Cure rate proportional odds models with spatial frailties for interval-censored data

Yiqi, B., Cancho, V. G., Louzada, F., & Suzuki, A. K. (2017) · Communications for Statistical Applications and Methods · doi:10.29220/csam.2017.24.6.605 · Cited by 5

The Pitfalls of Provisioning Exascale Networks: A Trace Replay Analysis for Understanding Communication Performance

Kenny, J. P., Sargsyan, K., Knight, S., Michelogiannakis, G., & Wilke, J. J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-92040-5_14 · Cited by 5

Parameter estimation with the Markov Chain Monte Carlo method aided by evolutionary neural networks in a water hammer model

Carvalho, R. C., Herzog, I. L., Orlande, H. R. B., Colaço, M. J., Madeira, I. M., & Chakraborti, N. (2023) · Computational and Applied Mathematics · doi:10.1007/s40314-022-02162-0 · Cited by 5

Brain network reconfiguration during reward prediction error processing

Bonna, K., Hulme, O. J., Steinkamp, S. R., Meder, D., van der Weij, M. E. C., Duch, W., & Finc, K. (2026) · Network Neuroscience · doi:10.1162/netn.a.544 · Cited by 4

Joint projections of North Pacific sea surface temperature from different global climate models

Beltrán, F., Sansó, B., Lemos, R. T., & Mendelssohn, R. (2012) · Environmetrics · doi:10.1002/env.2150 · Cited by 5

A Continuous Time-and-State Epidemic Model Fitted to Ordinal Categorical Data Observed on a Lattice at Discrete Times

Crété, R., Pumo, B., Soubeyrand, S., Didelot, F., & Caffier, V. (2013) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1007/s13253-013-0138-x · Cited by 5

A general threshold stress hybrid hazard model for lifetime data

Tojeiro, C. A. V., & Louzada, F. (2011) · Statistical Papers · doi:10.1007/s00362-011-0386-1 · Cited by 5

The log-Weibull-negative-binomial regression model under latent failure causes and presence of randomized activation schemes

Louzada, F., Cancho, V. G., & Yiqi, B. (2014) · Statistics · doi:10.1080/02331888.2014.925900 · Cited by 5

Quantification of uncertainty in velocity log upscaling by a Markov Chain Monte Carlo method

Gibson, R. L., & Hwang, K. (2009) · Second International Meeting for Applied Geoscience & Energy · doi:10.1190/1.3255383 · Cited by 5

A Bayesian Approach to Asymmetric Multidimensional Scaling

Okada, K. (2012) · Behaviormetrika · doi:10.2333/bhmk.39.49 · Cited by 5

Characterizing molecular adaptation: a hierarchical approach to assess the selective influence of amino acid properties

Datta, S., Prado, R., Rodríguez, A., & Escalante, A. A. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq532 · Cited by 5

On the Bayesian estimation and influence diagnostics for the Weibull-Negative-Binomial regression model with cure rate under latent failure causes

Yiqi, B., Cancho, V. G., & Louzada, F. (2016) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2015.1019150 · Cited by 5

Using semi‐Markov processes to study timeliness and tests used in the diagnostic evaluation of suspected breast cancer

Hubbard, R. A., Lange, J. M., Zhang, Y., Salim, B. A., Stroud, J., & Inoue, L. Y. T. (2016) · Statistics in Medicine · doi:10.1002/sim.7055 · Cited by 5

Dirichlet process crescent-signal mixture model for ground-penetrating radar signals

Kobayashi, M., & Nakano, K. (2014) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2014.7049007 · Cited by 5

Deterministic physical systems under uncertain initial conditions: the case of maximum entropy applied to projectile motion

Montecinos, A., Davis, S. M., & Peralta, J. (2018) · European Journal of Physics · doi:10.1088/1361-6404/aaaf0c · Cited by 5

A Bayesian model for lithology/fluid class prediction using a Markov mesh prior fitted from a training image

Tjelmeland, H., Luo, X., & Fjeldstad, T. M. (2019) · Geophysical Prospecting · doi:10.1111/1365-2478.12753 · Cited by 5

A multivariate projected Gamma model for directional data

Núñez‐Antonio, G., & Geneyro, E. (2019) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2019.1612910 · Cited by 5

A NON-GAUSSIAN FAMILY OF STATE-SPACE MODELS WITH EXACT MARGINAL LIKELIHOOD

Gamerman, D., dos Santos, T. R., & Franco, G. C. (2013) · Journal of Time Series Analysis · doi:10.1002/jtsa.12039 · Cited by 5

Collaborative Positioning Mechanism Using Bayesian Probabilistic Models for Industry Verticals

de Lima, C. T., Saloranta, J., & Latva‐aho, M. (2019) · International Symposium on Wireless Communication Systems · doi:10.1109/iswcs.2019.8877302 · Cited by 5

Faster MCMC for Gaussian latent position network models

Spencer, N. A., Junker, B. W., & Sweet, T. M. (2022) · Network Science · doi:10.1017/nws.2022.1 · Cited by 5

Variational Bayesian inversion for the reaction coefficient in space-time nonlocal diffusion equations

Song, X., Zheng, G.-H., & Jiang, L. (2021) · Advances in Computational Mathematics · doi:10.1007/s10444-021-09850-1 · Cited by 5

A Practical Algorithm for Volume Estimation based on Billiard Trajectories and Simulated Annealing

Chalkis, A., Emiris, I. Z., & Fisikopoulos, V. (2023) · ACM Journal of Experimental Algorithmics · doi:10.1145/3584182 · Cited by 5

Probabilistic medical image imputation via deep adversarial learning

Raad, R., Patel, D. V., Hsu, C.-C., Kothapalli, V., Ray, D., Varghese, B. A., Hwang, D., Gill, I. S., Duddalwar, V. A., & Oberai, A. A. (2022) · Engineering With Computers · doi:10.1007/s00366-022-01712-8 · Cited by 5

Capacity Assessment in Freight-Passengers Complex Railway Nodes: Trieste Case Study

Kianinejadoshah, A., & Ricci, S. (2022) · Infrastructures · doi:10.3390/infrastructures7080106 · Cited by 5

Bayesian factor models for the detection of coherent patterns in gene expression data

Mayrink, V. D., & Lucas, J. E. (2014) · Brazilian Journal of Probability and Statistics · doi:10.1214/13-bjps226 · Cited by 5

The use of nested sampling for prediction of infrastructure degradation under uncertainty

van Erp, H. R. N., & Orcesi, A. (2018) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2018.1441318 · Cited by 5

Sampling from manifold-restricted distributions using tangent bundle projections

Chua, A. J. K. (2019) · Statistics and Computing · doi:10.1007/s11222-019-09907-8 · Cited by 5

An application of a non-homogeneous Poisson model to study PM2.5 exceedances in Mexico City and Bogota

Suárez-Sierra, B. M., Rodrigues, E. R., & Tzintzun, G. (2021) · Journal of Applied Statistics · doi:10.1080/02664763.2021.1897972 · Cited by 5

Hybrid Approach in Microscale Transport Phenomena: Application to Biodiesel Synthesis in Micro-reactors

Junior, J. M. C., Pontes, P. C., Naveira‐Cotta, C. P., Tiwari, M. K., Balabani, S., & Cotta, R. M. (2019) · Green energy and technology · doi:10.1007/978-981-13-9012-8_20 · Cited by 5

Agent‐Directed Simulation for Systems Engineering

Barry, P., Koehler, M., & Tivnan, B. F. (2009) · Wiley eBooks · doi:10.1002/9783527627783.ch12 · Cited by 5

Competing working memory contents: perceptual over semantic prioritization and voluntary retrieval following retro-cueing

Cipriani, G. A., González‐García, C., Martín‐Arévalo, E., Lupiáñez, J., & Botta, F. (2024) · Visual Cognition · doi:10.1080/13506285.2024.2435652 · Cited by 6

Bayesian AMMI‐based indexes for genotype selection: Integrating novel stability measures for enhanced G × E inference

Nascimento, A. C. C., Nascimento, M., Sagae, V. S., Destro, V., Nardino, M., Olivoto, T., & Jarquín, D. (2025) · Crop Science · doi:10.1002/csc2.70140 · Cited by 4

Recruiting a skeleton crew—Methods for simulating and augmenting paleoanthropological data using Monte Carlo based algorithms

Courtenay, L. A., Aramendi, J., & González‐Aguilera, D. (2023) · American Journal of Biological Anthropology · doi:10.1002/ajpa.24754 · Cited by 4

The Transformed MG-Extended Exponential Distribution: Properties and Applications

Menberu, A. A., & Goshu, A. T. (2024) · Journal of Statistical Theory and Applications · doi:10.1007/s44199-024-00078-8 · Cited by 4

Fault Detection in an Electro-Hydrostatic Actuator Using Polyscale Complexity Measures and Bayesian Classification

Hosseinpour, S., Kinsner, W., Muthukumarana, S., & Sepehri, N. (2024) · IEEE Open Journal of Instrumentation and Measurement · doi:10.1109/ojim.2024.3487237 · Cited by 4

Optimal Guidance Mechanism for EV Charging Behavior and Its Impact Assessment on Distribution Network Hosting Capacity

Yang, X. P. O., Zhou, F., Xu, R., Zhong, Y., Jingjing, Y., & Yang, H. (2025) · Processes · doi:10.3390/pr13103107 · Cited by 4

Personalized fall risk assessment for long-term care services improvement

Chen, S., Kearns, W. D., Fozard, J. L., & Li, M. (2017) · Annual Reliability and Maintainability Symposium (RAMS) · doi:10.1109/ram.2017.7889692 · Cited by 4

Decoupling Shrinkage and Selection in Gaussian Linear Factor Analysis

Bolfarine, H., Carvalho, C. M., Lopes, H. F., & Murray, J. S. (2022) · Bayesian Analysis · doi:10.1214/22-ba1349 · Cited by 4

Clustering non-linear interactions in factor analysis

Amorim, E. D. C., & Mayrink, V. D. (2020) · METRON · doi:10.1007/s40300-020-00186-2 · Cited by 4

State estimation for aoristic models

van Lieshout, M. N. M., & Markwitz, R. L. (2022) · Scandinavian Journal of Statistics · doi:10.1111/sjos.12619 · Cited by 4

Assessing the impact of vaccination and medical resource allocation on infectious disease outbreak management: a case study of COVID-19 in Taiyuan City

Guo, J., Luo, Y., Ma, Y., Xu, S., Li, J., Wang, T., Lei, L., He, L., Yu, H., & Xie, J. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1368876 · Cited by 5

Bayesian Methods for Step-Stress Accelerated Test under Gamma Distribution with a Useful Reparametrization and an Industrial Data Application

Bakouch, H. S., Moala, F. A., Alghamdi, S. A., & Albalawi, O. (2024) · Mathematics · doi:10.3390/math12172747 · Cited by 4

Bayesian analysis of linear regression models with autoregressive symmetrical errors and incomplete data

Garay, A. M., Medina, F. L., de Freitas, S. T., & Lachos, V. H. (2024) · Statistical Papers · doi:10.1007/s00362-024-01612-7 · Cited by 4

Bayesian Inference for Long Memory Stochastic Volatility Models

Chaim, P., & Laurini, M. P. (2024) · Econometrics · doi:10.3390/econometrics12040035 · Cited by 4

Bayesian MCMC Estimation

Mills, J. A., & Parent, O. (2019) · Springer eBooks · doi:10.1007/978-3-642-36203-3_89-1 · Cited by 4

A more efficient Gibbs sampler estimation using steady-state simulation: applications to public health studies

Dunbar, M. X., Samawi, H. M., Vogel, R. L., & Yu, L. (2013) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2013.770857 · Cited by 4

Temporal Aggregation of Lognormal AR processes

Salazar, E., & Ferreira, M. A. R. (2011) · Journal of Time Series Analysis · doi:10.1111/j.1467-9892.2011.00723.x · Cited by 4

Assessing macro influence on Brazilian yield curve with affine models

Matsumura, M. S., & Moreira, A. R. B. (2009) · Applied Economics · doi:10.1080/00036840902762746 · Cited by 4

Mediated Interactions and Musical Expression—A Survey

Reidsma, D., Radha, M., & Nijholt, A. (2014) · Springer eBooks · doi:10.1007/978-1-4939-0536-2_4 · Cited by 4

A Two-Stage Maximum Entropy Prior of Location Parameter with a Stochastic Multivariate Interval Constraint and Its Properties

Kim, H.-J. (2016) · Entropy · doi:10.3390/e18050188 · Cited by 4

Bayesian mixture model averaging for identifying the different gene expressions of chickpea (Cicer arietinum) plant tissue

Astuti, A. B., Iriawan, N., Irhamah, & Kuswanto, H. (2016) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2016.1239112 · Cited by 4

A Parameter Estimation Method Using Linear Response Statistics

Harlim, J., Li, X., & Zhang, H. (2017) · Journal of Statistical Physics · doi:10.1007/s10955-017-1788-9 · Cited by 4

Skewed link-based regression models for misclassified binary data

Naranjo, L., Pérez, C. J., & Martín, J. (2018) · Revista de la Real Academia de Ciencias Exactas Físicas y Naturales Serie A Matemáticas · doi:10.1007/s13398-018-0571-3 · Cited by 4

An Integrated Pipeline for Combining in vitro Data and Mathematical Models Using a Bayesian Parameter Inference Approach to Characterize Spatio-temporal Chemokine Gradient Formation

Kalogiros, D. I., Russell, M. J., Bonneuil, W. V., Frattolin, J., Watson, D., Moore, J. E., Kypraios, T., & Brook, B. S. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.01986 · Cited by 4

A Bayesian approach for de-duplication in the presence of relational data

Sosa, J. C., & Rodríguez, A. (2022) · Journal of Applied Statistics · doi:10.1080/02664763.2022.2118678 · Cited by 4

Approximate Message Passing With a Colored Aliasing Model for Variable Density Fourier Sampled Images

Millard, C., Hess, A. T., Mailhé, B., & Tanner, J. (2020) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2020.3025228 · Cited by 4

Regression models for exceedance data: a new approach

Bourguignon, M., & do Nascimento, F. F. (2020) · Statistical Methods & Applications · doi:10.1007/s10260-020-00518-6 · Cited by 4

Bayesian generalizations of the integer-valued autoregressive model

F., P. C. M., Graziadei, H., & Lopes, H. F. (2020) · Journal of Applied Statistics · doi:10.1080/02664763.2020.1812544 · Cited by 4

Bayesian Hierarchical Model for Characterizing Electric Vehicle Charging Flexibility

Palomino, A., & Parvania, M. (2020) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm41954.2020.9281385 · Cited by 4

Bayesian longitudinal item response modeling with multivariate asymmetric serial dependencies

dos Santos, J. R. S., Azevedo, C., & Fox, J. (2021) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2021.1965604 · Cited by 4

The State Space Model in Finance

Triantafyllopoulos, K. (2021) · Springer texts in statistics · doi:10.1007/978-3-030-76124-0_7 · Cited by 4

Temperature and Salinity Inverted for a Mediterranean Eddy Captured With Seismic Data, Using a Spatially Iterative Markov Chain Monte Carlo Approach

Xiao, W., Sheen, K. L., Tang, Q., Shutler, J. D., Hobbs, R. W., & Ehmen, T. F. (2021) · Frontiers in Marine Science · doi:10.3389/fmars.2021.734125 · Cited by 4

Bayesian spatial econometrics: a software architecture

Kuschnig, N. (2022) · Journal of Spatial Econometrics · doi:10.1007/s43071-022-00023-w · Cited by 4

The Interplay between COVID-19 and the Economy in Canada

Albani, V., Grasselli, M. R., Pang, W., & Zubelli, J. P. (2022) · Journal of risk and financial management · doi:10.3390/jrfm15100476 · Cited by 4

Some Developments in Bayesian Hierarchical Linear Regression Modeling

Sosa, J., & Aristizabal, J.-P. (2022) · Revista Colombiana de Estadística · doi:10.15446/rce.v45n2.98988 · Cited by 6

Design by stochastic simulations of the thermal ablation treatment of tumors with high intensity focused ultrasound

Silva, R. L. S., Alaeian, M., & Orlande, H. R. B. (2023) · Numerical Heat Transfer Part B Fundamentals · doi:10.1080/10407790.2023.2228022 · Cited by 4

Analysis of Cascading Failures Due to Dynamic Load-Altering Attacks

Goodridge, M. P., Zocca, A., & Lakshminarayana, S. (2023) · IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) · doi:10.1109/smartgridcomm57358.2023.10333960 · Cited by 4

EM-Type algorithms for heavy-tailed logistic mixed models

Santos, C. C., & Loschi, R. H. (2017) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2017.1350678 · Cited by 4

A Bayesian Assessment of an Approximate Model for Unconfined Water Flow in Sloping Layered Porous Media

Chiachío, J., Chiachío, M., Sankararaman, S., & Prescott, D. R. (2018) · Transport in Porous Media · doi:10.1007/s11242-018-1094-2 · Cited by 4

Semiparametric bivariate modelling with flexible extremal dependence

Leonelli, M., & Gamerman, D. (2019) · Statistics and Computing · doi:10.1007/s11222-019-09878-w · Cited by 4

Reliability evaluation for vhf and uhf bands under different scenarios via propagation loss model

Li, X., Huang, H., Li, Y., & Li, Y. (2019) · Eksploatacja i Niezawodnosc - Maintenance and Reliability · doi:10.17531/ein.2019.3.3 · Cited by 4

Volatility forecasts using stochastic volatility models with nonlinear leverage effects

McAlinn, K., Ushio, A., & Nakatsuma, T. (2019) · Journal of Forecasting · doi:10.1002/for.2618 · Cited by 4

Semi-Parametric Cure Rate Proportional Odds Models with Spatial Frailties for Interval-Censored Data

Bao, Y., Cancho, V. G., Louzada, F., & Suzuki, A. K. (2019) · Advances in Data Science and Adaptive Analysis · doi:10.1142/s2424922x19500050 · Cited by 4

Prior Sensitivity Analysis in a Semi-Parametric Integer-Valued Time Series Model

Graziadei, H., Lijoi, A., Lopes, H. F., F., P. C. M., & Prünster, I. (2020) · Entropy · doi:10.3390/e22010069 · Cited by 4

Spatio-temporal modelling of hydro-meteorological derived risk using a Bayesian approach: a case study in Venezuela

Villalta, D., de Guenni, L. B., & Sajo-Castelli, A. M. (2020) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-020-01783-3 · Cited by 4

Rainfall trends over a North Atlantic small island in the period 1937/1938–2016/2017 and an early climate teleconnection

Espinosa, L. Á., Portela, M. M., & Rodrigues, R. R. (2021) · Theoretical and Applied Climatology · doi:10.1007/s00704-021-03547-7 · Cited by 4

Bayesian Estimation of Adsorption and Desorption Parameters for Pore Scale Transport

Grigoriev, V. V., & Vabishchevich, P. N. (2021) · Mathematics · doi:10.3390/math9161974 · Cited by 4

Real-time covariance estimation for the local level model

Triantafyllopoulos, K. (2010) · Journal of Time Series Analysis · doi:10.1111/j.1467-9892.2010.00686.x · Cited by 4

Monte Carlo Simulation of Dynamic Problem Using Model Order Reduction Technique Highlighting on Tail Probability

TAKANO, N., Asai, M., & Okamoto, K. (2012) · Journal of Computational Science and Technology · doi:10.1299/jcst.6.169 · Cited by 4

Double generalized linear model for tissue culture proportion data: a Bayesian perspective

Vieira, A. M. C., Leandro, R. A., Demétrio, C. G. B., & Molenberghs, G. (2010) · Journal of Applied Statistics · doi:10.1080/02664763.2010.529875 · Cited by 4

Bayesian learning of a search region for pedestrian detection

Baek, J., Hong, S., Kim, J., & Kim, E. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2329-z · Cited by 4

Bayesian outlier analysis in binary regression

de Souza, A. D. P., & Migon, H. S. (2010) · Journal of Applied Statistics · doi:10.1080/02664760903031153 · Cited by 4

Bayesian inference for the information gain model

Stringer, S., Borsboom, D., & Wagenmakers, E. (2011) · Behavior Research Methods · doi:10.3758/s13428-010-0057-5 · Cited by 4

The limiting distribution of the Gibbs sampler for the intrinsic conditional autoregressive model

Ferreira, M. A. R. (2019) · Brazilian Journal of Probability and Statistics · doi:10.1214/19-bjps435 · Cited by 4

Bayesian Markov Chain Monte Carlo Estimation

Mills, J. A., & Parent, O. (2021) · Springer eBooks · doi:10.1007/978-3-662-60723-7_89 · Cited by 4

Jump Risk Premia in Short-Term Spread Options: Evidence from the German Electricity MarketPreprint

Marckhoff, J., & Mück, M. (2009) · SSRN Electronic Journal · doi:10.2139/ssrn.1403817 · Cited by 4

A Report on the Future of Finance, Future of Risk, and Future of Quant: Risk, Uncertainty, and, Profit for the Cyber Era: Model Risk Management of Cyber Insurance Models using Quantitative Finance and Advanced AnalyticsPreprint

Malhotra, Y. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2553547 · Cited by 4

Markov Chain Generative Adversarial Neural Networks for Solving Bayesian Inverse Problems in Physics ApplicationsPreprint

Mücke, N. T., Sanderse, B., Bohté, S. M., & Oosterlee, C. W. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3991779 · Cited by 5

Many-objective Self-adaptation under Model Uncertainty

Camilli, M., Mirandola, R., & Scandurra, P. (2025) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3719349 · Cited by 3

Understanding risky lane-changing decisions in expressway weaving areas: a hybrid causal inference and hierarchical drift-diffusion model

Guo, Y., Gu, X., Sze, N. N., Feng, T., Li, Y., & Chen, Y. (2025) · Transportmetrica A Transport Science · doi:10.1080/23249935.2025.2532104 · Cited by 3

Presence-Only for Marked Point Process Under Preferential Sampling

Moreira, G. A., Menezes, R., & Wise, L. (2023) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1007/s13253-023-00558-x · Cited by 3

Lapse risk modeling in insurance: a Bayesian mixture approach

Lobo, V. G. R., Fonseca, T. C. O., & Alves, M. B. (2023) · Annals of Actuarial Science · doi:10.1017/s1748499523000180 · Cited by 3

Regression in extremes using the four-parameter Kappa distribution

Costa, L. L. D. S., & do Nascimento, F. F. (2023) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2023.2266783 · Cited by 3

Absolute Poverty Measurement with Minimum Food Needs: A New Inverse Method for Advanced Economies

Menyhért, B. (2024) · Social Indicators Research · doi:10.1007/s11205-024-03368-x · Cited by 3

The impact of hydroclimate-driven periodic runoff on hydropower production and management

Hao, S., Wörman, A., & Brandimarte, L. (2024) · Scientific Reports · doi:10.1038/s41598-024-76461-3 · Cited by 3

Assessing presence of per- and polyfluoroalkyl substances (PFAS) in the Indian River Lagoon: A Bayesian approach to understanding the impact of environmental stressors

Kumar, S., Borrés, S. E. S., Bonzongo, J. J., Quiñones, K. Y. D., & Jutla, A. S. (2025) · Chemosphere · doi:10.1016/j.chemosphere.2025.144287 · Cited by 3

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)