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

Bayesian evaluation of groundwater age distribution using radioactive tracers and anthropogenic chemicals

Massoudieh, A., Sharifi, S., & Solomon, D. K. (2012) · Water Resources Research · doi:10.1029/2012wr011815 · Cited by 61

Spatial stochastic frontier models: accounting for unobserved local determinants of inefficiency

Schmidt, A. M., Moreira, A. R. B., Helfand, S. M., & Fonseca, T. C. O. (2008) · Journal of Productivity Analysis · doi:10.1007/s11123-008-0122-6 · Cited by 60

A half-century of Baarda’s concept of reliability: a review, new perspectives, and applications

Rofatto, V. F., Matsuoka, M. T., Klein, I., Veronez, M. R., Bonimani, M. L. S., & Lehmann, R. (2018) · Survey Review · doi:10.1080/00396265.2018.1548118 · Cited by 59

Bayesian Inversion for a Stress‐Driven Model of Afterslip and Viscoelastic Relaxation: Method and Application to Postseismic Deformation Following the 2011 M W 9.0 Tohoku‐Oki Earthquake

Fukuda, J., & Johnson, K. M. (2021) · Journal of Geophysical Research Solid Earth · doi:10.1029/2020jb021620 · Cited by 58

A Bayesian approach for parameter estimation and prediction using a computationally intensive model

Higdon, D., McDonnell, J., Schunck, N. F., Sarich, J., & Wild, S. M. (2015) · Journal of Physics G Nuclear and Particle Physics · doi:10.1088/0954-3899/42/3/034009 · Cited by 57

The intraparietal sulcus governs multisensory integration of audiovisual information based on task difficulty

Regenbogen, C., Seubert, J., Johansson, E., Finkelmeyer, A. E., Andersson, P. U., & Lundström, J. N. (2017) · Human Brain Mapping · doi:10.1002/hbm.23918 · Cited by 53

A Gentle Introduction to Bayesian Network Meta-Analysis Using an Automated R Package

Liu, Y., Béliveau, A., Wei, Y., Chen, M. Y., Record-Lemon, R., Kuo, P., Pritchard, E., Tang, X., & Chen, G. (2022) · Multivariate Behavioral Research · doi:10.1080/00273171.2022.2115965 · Cited by 58

A flexible model for survival data with a cure rate: a Bayesian approach

Cancho, V. G., Rodrigues, J., & de Castro, M. (2010) · Journal of Applied Statistics · doi:10.1080/02664760903254052 · Cited by 56

Bayesian Reliability: Combining Information

Wilson, A. G., & Fronczyk, K. M. (2016) · Quality Engineering · doi:10.1080/08982112.2016.1211889 · Cited by 55

Suspended sediment source apportionment in Chesapeake Bay watershed using Bayesian chemical mass balance receptor modeling

Massoudieh, A., Gellis, A. C., Banks, W. S. L., & Wieczorek, M. (2012) · Hydrological Processes · doi:10.1002/hyp.9429 · Cited by 54

Multilevel modelling as a tool to include variability and uncertainty in quantitative microbiology and risk assessment. Thermal inactivation of Listeria monocytogenes as proof of concept

Garre, A., Zwietering, M. H., & den Besten, H. M. W. (2020) · Food Research International · doi:10.1016/j.foodres.2020.109374 · Cited by 54

Inverse Problems in a Bayesian Setting

Matthies, H. G., Zander, E., Rosić, B., Litvinenko, A., & Pajonk, O. (2016) · Computational methods in applied sciences · doi:10.1007/978-3-319-27996-1_10 · Cited by 53

On the flexibility of the design of multiple try Metropolis schemes

Martino, L., & Read, J. (2013) · Computational Statistics · doi:10.1007/s00180-013-0429-2 · Cited by 53

A Bayesian Long‐term Survival Model Parametrized in the Cured Fraction

de Castro, M., Cancho, V. G., & Rodrigues, J. (2009) · Biometrical Journal · doi:10.1002/bimj.200800199 · Cited by 53

A hierarchical Bayesian approach for estimation of photosynthetic parameters of C3plants

PATRICK, L. D., Ogle, K., & Tissue, D. T. (2009) · Plant Cell & Environment · doi:10.1111/j.1365-3040.2009.02029.x · Cited by 52

A Bayesian Approach for Modeling Cattle Movements in the United States: Scaling up a Partially Observed Network

Lindström, T., Grear, D. A., Buhnerkempe, M., Webb, C. T., Miller, R. S., Portacci, K., & Wennergren, U. (2013) · PLoS ONE · doi:10.1371/journal.pone.0053432 · Cited by 52

Global analysis of LISA data with Galactic binaries and massive black hole binaries

Strub, S. H., Ferraioli, L., Schmelzbach, C., Stähler, S. C., & Giardini, D. (2024) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.110.024005 · Cited by 51

Accelerated Bayesian Inference for the Estimation of Spatially Varying Heat Flux in a Heat Conduction Problem

Orlande, H. R. B., Dulikravich, G. S., Neumayer, M., Watzenig, D., & Colaço, M. J. (2013) · Numerical Heat Transfer Part A Applications · doi:10.1080/10407782.2013.812008 · Cited by 51

General mixture item response models with different item response structures: Exposition with an application to Likert scales

Tijmstra, J., Bolsinova, M. A., & Jeon, M. (2018) · Behavior Research Methods · doi:10.3758/s13428-017-0997-0 · Cited by 51

Statistical models for overdispersion in the frequency of peaks over threshold data for a flow series

Eastoe, E., & Tawn, J. A. (2010) · Water Resources Research · doi:10.1029/2009wr007757 · Cited by 49

On using the cosmic microwave background shift parameter in tests of models of dark energy

Elgarøy, Ø., & Multamäki, T. (2007) · Springer Link (Chiba Institute of Technology) · doi:10.1051/0004-6361:20077292/pdf · Cited by 49

Ensuring identifiability in hierarchical mixed effects Bayesian models

Ogle, K., & Barber, J. J. (2020) · Ecological Applications · doi:10.1002/eap.2159 · Cited by 48

Antecedent Conditions Influence Soil Respiration Differences in Shrub and Grass Patches

Cable, J. M., Ogle, K., Barron‐Gafford, G. A., Bentley, L. P., Cable, W., Scott, R. L., Williams, D. G., & Huxman, T. E. (2013) · Ecosystems · doi:10.1007/s10021-013-9679-7 · Cited by 47

A Bayesian statistical model for deriving the predictive distribution of hydroclimatic variables

Tyralis, H., & Koutsoyiannis, D. (2013) · Climate Dynamics · doi:10.1007/s00382-013-1804-y · Cited by 46

Statistically Efficient Thinning of a Markov Chain Sampler

Owen, A. B. (2017) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2017.1336446 · Cited by 46

Bayesian phylogenetics illuminate shallower relationships among Trans-Himalayan languages in the Tibet-Arunachal area

Wu, M.-S., Bodt, T. A., & Tresoldi, T. (2022) · Linguistics of the Tibeto-Burman Area · doi:10.1075/ltba.21019.wu · Cited by 46

An Integrated Bayesian Model for DIF Analysis

Soares, T. M., Gonçalves, F. B., & Gamerman, D. (2009) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998609332752 · Cited by 45

Permafrost thaw affects boreal deciduous plant transpiration through increased soil water, deeper thaw, and warmer soils

Cable, J. M., Ogle, K., Bolton, W. R., Bentley, L. P., Romanovsky, V. E., Iwata, H., Harazono, Y., & Welker, J. M. (2013) · Ecohydrology · doi:10.1002/eco.1423 · Cited by 44

Optimization Algorithms for Computational Systems Biology

Reali, F., Priami, C., & Marchetti, L. (2017) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2017.00006 · Cited by 44

Physiological responses of two contrasting desert plant species to precipitation variability are differentially regulated by soil moisture and nitrogen dynamics

PATRICK, L. D., Ogle, K., Bell, C., Zak, J. C., & Tissue, D. T. (2009) · Global Change Biology · doi:10.1111/j.1365-2486.2008.01750.x · Cited by 44

Batch-to-Batch Variation: A Key Component for Modeling Chemical Manufacturing Processes

Mockus, L., Peterson, J. J., Laínez‐Aguirre, J. M., & Reklaitis, G. V. (2014) · Organic Process Research & Development · doi:10.1021/op500244f · Cited by 43

Parameter identification for phase-field modeling of fracture: a Bayesian approach with sampling-free update

Wu, T., Rosić, B., Lorenzis, L. D., & Matthies, H. G. (2020) · Computational Mechanics · doi:10.1007/s00466-020-01942-x · Cited by 43

Developments in data science solutions for carnivore tooth pit classification

Courtenay, L. A., Herranz‐Rodrigo, D., González‐Aguilera, D., & Yravedra, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-89518-4 · Cited by 44

Statistical Calibration of Climate System Properties

Sansó, B., & Forest, C. E. (2009) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/j.1467-9876.2009.00669.x · Cited by 42

Bayesian Cultural Consensus Theory

Oravecz, Z., Vandekerckhove, J., & Batchelder, W. H. (2014) · Field Methods · doi:10.1177/1525822x13520280 · Cited by 43

Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

Wainwright, H. M., Liljedahl, A., Dafflon, B., Ulrich, C., Peterson, J., Gusmeroli, A., & Hubbard, S. S. (2017) · The Cryosphere · doi:10.5194/tc-11-857-2017 · Cited by 42

The Processing-Speed Impairment in Psychosis Is More Than Just Accelerated Aging

Mathias, S., Knowles, E., Barrett, J., Leach, O. K., Buccheri, S., Beetham, T., Blangero, J., Poldrack, R., & Glahn, D. (2017) · Schizophrenia bulletin · doi:10.1093/schbul/sbw168 · Cited by 39

Modelling historical changes in the force-of-infection of Chagas disease to inform control and elimination programmes: application in Colombia

Cucunubá, Z. M., Nouvellet, P., Conteh, L., Vera, M. J., Angulo, V. M., Dib, J. C., Parra-Henao, G., & Basáñez, M.-G. (2017) · BMJ Global Health · doi:10.1136/bmjgh-2017-000345 · Cited by 42

Predicting intervention effect for COVID-19 in Japan: state space modeling approach

Kobayashi, G., Sugasawa, S., Tamae, H., & Ozu, T. (2020) · BioScience Trends · doi:10.5582/bst.2020.03133 · Cited by 42

Estimating the Risk of COVID-19 Death during the Course of the Outbreak in Korea, February–May 2020

Shim, E., Mizumoto, K., Choi, W., & Chowell, G. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9061641 · Cited by 41

Using copulas for rating weather index insurance contracts

Bokusheva, R. (2018) · Journal of Applied Statistics · doi:10.1080/02664763.2017.1420146 · Cited by 41

The Hastings algorithm at fifty

Dunson, D. B., & Johndrow, J. E. (2019) · Biometrika · doi:10.1093/biomet/asz066 · Cited by 41

Modelling zero-inflated spatio-temporal processes

Fernandes, M. V., Schmidt, A. M., & Migon, H. S. (2009) · Statistical Modelling · doi:10.1177/1471082x0800900102 · Cited by 42

Probability Theory as Logic: Data Assimilation for Multiple Source Reconstruction

Yee, E. (2011) · Pure and Applied Geophysics · doi:10.1007/s00024-011-0384-1 · Cited by 40

Bayesian hierarchical approach and geophysical data sets for estimation of reactive facies over plume scales

Wainwright, H. M., Chen, J., Sassen, D. S., & Hubbard, S. S. (2014) · Water Resources Research · doi:10.1002/2013wr013842 · Cited by 40

Surveillance of Space using passive radar and the Murchison Widefield Array

Palmer, J. E., Hennessy, B., Rutten, M., Merrett, D., Tingay, S. J., Kaplan, D. L., Tremblay, S. E., Ord, S. M., Morgan, J. S., & Wayth, R. B. (2017) · IEEE Radar Conference (RadarConf) · doi:10.1109/radar.2017.7944483 · Cited by 40

Hemorrhagic fever with renal syndrome in China: Mechanisms on two distinct annual peaks and control measures

Li, L., Wang, C., Wang, S., Li, M., Yakob, L., Cazelles, B., Jin, Z., & Zhang, W. (2018) · International Journal of Biomathematics · doi:10.1142/s1793524518500304 · Cited by 40

Quickest Detection of False Data Injection Attacks in Smart Grid with Dynamic Models

Nath, S., Akingeneye, I., Wu, J., & Han, Z. (2019) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2019.2936587 · Cited by 40

A two-stage Markov chain Monte Carlo method for seismic inversion and uncertainty quantification

Stuart, G. K., Minkoff, S. E., & Pereira, F. (2019) · Geophysics · doi:10.1190/geo2018-0893.1 · Cited by 40

Bayesian Instrumental Variables: Priors and Likelihoods

Lopes, H. F., & Polson, N. G. (2013) · Econometric Reviews · doi:10.1080/07474938.2013.807146 · Cited by 40

Bayesian analysis of quantile regression for censored dynamic panel data

Kobayashi, G., & Kozumi, H. (2011) · Computational Statistics · doi:10.1007/s00180-011-0263-3 · Cited by 39

Evaluation of different packing media for anoxic H2S control in biogas

Soreanu, G., Béland, M., Falletta, P., Ventresca, B., & Seto, P. (2009) · Environmental Technology · doi:10.1080/09593330902998314 · Cited by 39

Measuring Ambiguity Aversion

Gallant, A. R., Jahan‐Parvar, M. R., & Liu, H. (2015) · Finance and Economics Discussion Series · doi:10.17016/feds.2015.105 · Cited by 39

Bayesian inference of selection in a heterogeneous environment from genetic time‐series data

Gompert, Z. (2015) · Molecular Ecology · doi:10.1111/mec.13323 · Cited by 39

Interplate Coupling Distribution Along the Nankai Trough in Southwest Japan Estimated From the Block Motion Model Based on Onshore GNSS and Seafloor GNSS/A Observations

Kimura, H., Tadokoro, K., & Ito, T. (2019) · Journal of Geophysical Research Solid Earth · doi:10.1029/2018jb016159 · Cited by 39

Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties

Zhou, Z., Zabaras, N. J., & Tartakovsky, D. M. (2022) · Water Resources Research · doi:10.1029/2021wr031438 · Cited by 38

Sediment source apportionment in Laurel Hill Creek, PA, using Bayesian chemical mass balance and isotope fingerprinting

Stewart, H. A., Massoudieh, A., & Gellis, A. C. (2014) · Hydrological Processes · doi:10.1002/hyp.10364 · Cited by 38

Labor market entry and earnings dynamics: Bayesian inference using mixtures‐of‐experts Markov chain clustering

Frühwirth‐Schnatter, S., Pamminger, C., Weber, A. M., & Winter‐Ebmer, R. (2011) · Journal of Applied Econometrics · doi:10.1002/jae.1249 · Cited by 38

Conflict Diagnostics in Directed Acyclic Graphs, with Applications in Bayesian Evidence Synthesis

Presanis, A. M., Ohlssen, D., Spiegelhalter, D. J., & Angelis, D. D. (2013) · Statistical Science · doi:10.1214/13-sts426 · Cited by 38

Shrub encroachment alters sensitivity of soil respiration to temperature and moisture

Cable, J. M., Barron‐Gafford, G. A., Ogle, K., Pavao‐Zuckerman, M. A., Scott, R. L., Williams, D. G., & Huxman, T. E. (2011) · Journal of Geophysical Research Atmospheres · doi:10.1029/2011jg001757 · Cited by 38

Integrating Misidentification and OOD Detection for Reliable Fault Diagnosis of High-Speed Train Bogie

Feng, Y., Chen, J., Xie, Z., Xie, J., Pan, T., Li, C., & Zhao, Q. Z. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3377813 · Cited by 37

Quantifying intrinsic and extrinsic noise in gene transcription using the linear noise approximation: An application to single cell data

Finkenstädt, B., Woodcock, D. J., Komorowski, M., Harper, C. V., Davis, J. R., White, M., & Rand, D. A. J. (2013) · The Annals of Applied Statistics · doi:10.1214/13-aoas669 · Cited by 37

Modeling the effect of contaminated objects for the transmission dynamics of COVID-19 pandemic with self protection behavior changes

Mekonen, K. G., Habtemichael, T. G., & Feyissa, S. (2020) · Results in Applied Mathematics · doi:10.1016/j.rinam.2020.100134 · Cited by 37

Bayesian Statistics in Sociology: Past, Present, and Future

Lynch, S. M., & Bartlett, B. J. (2019) · Annual Review of Sociology · doi:10.1146/annurev-soc-073018-022457 · Cited by 36

Treatment Effects: A Bayesian Perspective

Heckman, J. J., Lopes, H. F., & Piatek, R. (2013) · Econometric Reviews · doi:10.1080/07474938.2013.807103 · Cited by 32

Identification of contaminant source architectures—A statistical inversion that emulates multiphase physics in a computationally practicable manner

Koch, J., & Nowak, W. (2016) · Water Resources Research · doi:10.1002/2015wr017894 · Cited by 36

Estimating Direct and Indirect Protective Effect of Influenza Vaccination in the United States

Arinaminpathy, N., Kim, I. K., Gargiullo, P. M., Haber, M., Foppa, I., Gambhir, M., & Bresee, J. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx037 · Cited by 35

Introduction to Dynamic Linear Models for Time Series Analysis

Laine, M. (2019) · Springer geophysics · doi:10.1007/978-3-030-21718-1_4 · Cited by 39

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

Rofatto, V. F., Matsuoka, M. T., Klein, I., Veronez, M. R., & da Silveira, L. G. (2020) · Remote Sensing · doi:10.3390/rs12050860 · Cited by 35

Performance evaluation for manufacturing systems under control-limit maintenance policy

Gu, X., Guo, W., & Jin, X. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.03.003 · Cited by 35

Quantifying the Hurricane Catastrophe Risk to Offshore Wind Power

Rose, S., Jaramillo, P., Small, M. J., & Apt, J. (2013) · Risk Analysis · doi:10.1111/risa.12085 · Cited by 35

MONTHLY PERIODIC OUTBREAK OF HEMORRHAGIC FEVER WITH RENAL SYNDROME IN CHINA

Li, L. (2016) · Journal of Biological Systems · doi:10.1142/s0218339016500261 · Cited by 34

A Bayesian peaks-over-threshold analysis of floods in the Itajaí-açu River under stationarity and nonstationarity

Silva, A. T., Portela, M. M., Naghettini, M., & Fernandes, W. D. S. (2015) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-015-1184-4 · Cited by 34

Addressing voice recording replications for tracking Parkinson’s disease progression

Naranjo, L., Pérez, C. J., & Martín, J. (2016) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-016-1512-y · Cited by 34

Bayesian inference in measurement error models from objective priors for the bivariate normal distribution

de Castro, M., & Vidal, I. (2016) · Statistical Papers · doi:10.1007/s00362-016-0863-7 · Cited by 34

An Adaptive Metropolis-Hastings Optimization Algorithm of Bayesian Estimation in Non-Stationary Flood Frequency Analysis

Xu, W., Jiang, C., Yan, L., Li, L., & Liu, S. (2018) · Water Resources Management · doi:10.1007/s11269-017-1873-5 · Cited by 34

Measuring dependence in joint distributions of yield and weather variables

Bokusheva, R. (2011) · Agricultural Finance Review · doi:10.1108/00021461111128192 · Cited by 34

The geography of Spanish bank branches

Alamá‐Sabater, L., Conesa, D., Forte, A., & Tortosa‐Ausina, E. (2014) · Journal of Applied Statistics · doi:10.1080/02664763.2014.980792 · Cited by 34

Gross primary production variability associated with meteorology, physiology, leaf area, and water supply in contrasting woodland and grassland semiarid riparian ecosystems

Jenerette, G. D., Scott, R. L., Barron‐Gafford, G. A., & Huxman, T. E. (2009) · Journal of Geophysical Research Atmospheres · doi:10.1029/2009jg001074 · Cited by 34

Conflict Diagnostics in Directed Acyclic Graphs, with Applications in Bayesian Evidence Synthesis

Presanis, A. M., Ohlssen, D. I., Spiegelhalter, D. J., & Angelis, D. D. (2013) · Apollo (University of Cambridge) · doi:10.17863/cam.34842 · Cited by 34

Efficient Sampling for Gaussian Linear Regression With Arbitrary Priors

Hahn, P. R., He, J., & Lopes, H. F. (2018) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2018.1482762 · Cited by 33

The Poisson–exponential distribution: a Bayesian approach

Louzada, F., Cancho, V. G., & Barriga, G. D. C. (2011) · Journal of Applied Statistics · doi:10.1080/02664763.2010.491862 · Cited by 33

Bayesian inference for stochastic epidemic models with time-inhomogeneous removal rates

Boys, R. J., & Pr, G. (2007) · Journal of Mathematical Biology · doi:10.1007/s00285-007-0081-y · Cited by 32

Sequential Monte Carlo Samplers for capital allocation under copula-dependent risk models

Targino, R. S., Peters, G. W., & Shevchenko, P. V. (2015) · Insurance Mathematics and Economics · doi:10.1016/j.insmatheco.2015.01.007 · Cited by 32

Parameter and uncertainty estimation for process‐oriented population and distribution models: data, statistics and the niche

Marion, G., McInerny, G., Pagel, J., Catterall, S., Cook, A. R., Hartig, F., & O’Hara, R. B. (2012) · Journal of Biogeography · doi:10.1111/j.1365-2699.2012.02772.x · Cited by 32

Toward a Sparse Bayesian Markov Random Field Approach to Hyperspectral Unmixing and Classification

Chen, P., Nelson, J. D. B., & Tourneret, J. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2622401 · Cited by 32

Uncertainty quantification and propagation in nuclear density functional theory

Schunck, N. F., McDonnell, J. D., Higdon, D., Sarich, J., & Wild, S. M. (2015) · The European Physical Journal A · doi:10.1140/epja/i2015-15169-9 · Cited by 32

Stochastic one-way carsharing systems with dynamic relocation incentives through preference learning

Liu, Y., Xie, J., & Chen, N. (2022) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2022.102884 · Cited by 32

Bayesian Estimation for Poisson-exponential Model under Progressive Type-II Censoring Data with Binomial Removal and Its Application to Ovarian Cancer Data

Singh, S. K., Singh, U., & Kumar, M. (2014) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2014.948189 · Cited by 32

How Challenging are Bebras Tasks?

Bellettini, C., Lonati, V., Malchiodi, D., Monga, M., Morpurgo, A., & Torelli, M. (2015) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/2729094.2742603 · Cited by 32

A review of Markov Chain Monte Carlo and information theory tools for inverse problems in subsurface flow

Yustres, Á., Asensio, L., Alonso, J., & Navarro, V. (2011) · Computational Geosciences · doi:10.1007/s10596-011-9249-z · Cited by 32

A Simple Method for Comparing Complex Models: Bayesian Model Comparison for Hierarchical Multinomial Processing Tree Models Using Warp-III Bridge Sampling

Gronau, Q. F., Wagenmakers, E., Heck, D. W., & Matzke, D. (2018) · Psychometrika · doi:10.1007/s11336-018-9648-3 · Cited by 32

Bayesian inference on stochastic gene transcription from flow cytometry data

Tiberi, S., Walsh, M. D., Cavallaro, M., Hebenstreit, D., & Finkenstädt, B. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty568 · Cited by 30

An Efficient Multi-Objective Optimization Method for Use in the Design of Marine Protected Area Networks

Fox, A. D., Corne, D., Adame, C. G. M., Polton, J. A., Henry, L., & Roberts, J. M. (2019) · Frontiers in Marine Science · doi:10.3389/fmars.2019.00017 · Cited by 34

Brief Research Report: Bayesian Versus REML Estimations With Noninformative Priors in Multilevel Single-Case Data

Baek, E., Beretvas, S. N., Noortgate, W. V. D., & Ferron, J. M. (2019) · The Journal of Experimental Education · doi:10.1080/00220973.2018.1527280 · Cited by 31

High-Resolution ISAR Imaging Under Low SNR With Sparse Stepped-Frequency Chirp Signals

Zhou, F., Tian, X., Wang, Y., Wang, X., & Bai, X. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3045971 · Cited by 31

Bayesian analysis of censored linear regression models with scale mixtures of normal distributions

Garay, A. M., Bolfarine, H., Lachos, V. H., & Cabral, C. R. B. (2015) · Journal of Applied Statistics · doi:10.1080/02664763.2015.1048671 · Cited by 31

Space-time Bayesian small area disease risk models: development and evaluation with a focus on cluster detection

Hossain, M. M., & Lawson, A. B. (2008) · Environmental and Ecological Statistics · doi:10.1007/s10651-008-0102-z · Cited by 31

Bayesian Hypothesis Testing: An Alternative to Null Hypothesis Significance Testing (NHST) in Psychology and Social Sciences

Ortega, A., & Navarrete, G. (2017) · Bayesian Inference · doi:10.5772/intechopen.70230 · Cited by 31

A Vine-copula Based Adaptive MCMC Sampler for Efficient Inference of Dynamical Systems

Schmidl, D., Czado, C., Hug, S., & Theis, F. J. (2013) · Bayesian Analysis · doi:10.1214/13-ba801 · Cited by 30

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)