Bayesian Data Analysis

Compulsion is associated with impaired goal-directed and habitual learning and responding in obsessive-compulsive disorder

Yu, Q., Gao, F., Li, C., Xia, J. L., Cao, Y., Wang, X., Xiao, C., Lu, J., Liu, Q., Fan, J., et al. (2024) · International Journal of Clinical and Health Psychology · doi:10.1016/j.ijchp.2024.100531 · Cited by 4

Bayesian inference-based stochastic group priorities acceptability analysis for group decision making with triangular fuzzy preference relations

Liu, J., Wei, W., Shao, L., Yang, S., Zhou, L., & Jin, F. (2025) · Information Sciences · doi:10.1016/j.ins.2025.122287 · Cited by 4

AdaptEx: A Self-Service Contextual Bandit Platform

Black, W., İlhan, E., Marchini, A., & Markeviciute, V. K. (2023) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3604915.3608870 · Cited by 4

Linear learning in changing environments

Klumpp, T. (2005) · Journal of Economic Dynamics and Control · doi:10.1016/j.jedc.2005.08.007 · Cited by 4

Robust Gaussian process regression based on bias trimming

Chi, J., Mao, Z., & Jia, M. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111605 · Cited by 3

Implementing Bayesian inference on a stochastic CO2-based grey-box model

Yan, S., Zou, J., Shu, C., Berquist, J., Brochu, V., Veillette, M., Hou, D., Duchaine, C. S., Zhou, L. (., Zhai, Z. J., et al. (2025) · Indoor Environments · doi:10.1016/j.indenv.2025.100079 · Cited by 5

Genetic data reveal wintering ground affiliation of humpback whales from the Mexican Central Pacific

Llamas‐González, M., Ortega-Ortiz, C. D., Enríquez‐Paredes, L. M., González-Peral, Ú., Bautista‐Guerrero, E., & Martien, K. K. (2023) · Marine Mammal Science · doi:10.1111/mms.13053 · Cited by 3

Natural resource extraction, energy efficiency moderation, and sustainable development in the Gulf Cooperation Council countries

ALNSOUR, J. A. (2025) · Resources Policy · doi:10.1016/j.resourpol.2025.105750 · Cited by 3

Seasonal distribution of bloater (Coregonus hoyi) in the waters of Lake Huron surrounding the Bruce Peninsula

Harford, W. J., Muir, A. M., Harpur, C., Crawford, S. S., Parker, S. R., & Mandrak, N. E. (2012) · Journal of Great Lakes Research · doi:10.1016/j.jglr.2012.03.006 · Cited by 3

Practical robust estimators for the imprecise Dirichlet model

Hütter, M. (2008) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2008.03.020 · Cited by 3

Bayesian adaptive enrichment design in multi-arm clinical trials: The BayesAET package for R users

Zhan, D., Ouyang, Y., Vila‐Rodriguez, F., Karim, M. E., & Wong, H. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108833 · Cited by 2

Bayesian extreme learning

Ardakani, O. M. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128164 · Cited by 2

Quantitative viral dynamics: Methods for parameter estimation

Tower, A., Owens, K. L., Esmaeili, S., Schiffer, J. T., Reeves, D. B., & Schwartz, E. J. (2025) · Virology · doi:10.1016/j.virol.2025.110631 · Cited by 2

Current and future carbon stocks of a dominant forest plantation species, Cryptomeria japonica, throughout Japan

Nakahata, R., Egusa, T., & Kumagai, T. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.126981 · Cited by 3

A Bayesian reliability methodology for rock structures considering uncertainties in random field characteristics with limited data of rock properties

Kumar, A., & Tiwari, G. (2025) · International Journal of Rock Mechanics and Mining Sciences · doi:10.1016/j.ijrmms.2025.106249 · Cited by 3

Gaussian process latent variable model and Bayesian inference for non-parametric failure modeling applied to ship engine

BahooToroody, A., Abaei, M. M., Zio, E., Goerlandt, F., & Chaal, M. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111611 · Cited by 2

Unraveling the relationship between surface deformation and groundwater dynamics in karst terrains using multi-source remote sensing data

Gu, S., Zhou, Y., Jing, Y., Zhang, Z., Shao, J., Li, S., Chen, J., Zhao, Y., She, X., & Li, Y. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.134129 · Cited by 2

Modelling and understanding the hierarchy in a mixture of experts using multiple catchment descriptors

Jeremiah, E., Marshall, L. A., & Sharma, A. (2013) · Journal of Hydrology · doi:10.1016/j.jhydrol.2013.09.049 · Cited by 2

Rapid prototyping for quantifying belief weights of competing hypotheses about emergent diseases

Robertson, E. P., Walsh, D. P., Martin, J., Work, T. M., Kellogg, C. A., Evans, J. S., Barker, V., Hawthorn, A., Aeby, G. S., Paul, V. J., et al. (2023) · Journal of Environmental Management · doi:10.1016/j.jenvman.2023.117668 · Cited by 2

Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations

Bridgman, W., Jones, R. E., & Khalil, M. (2023) · Probabilistic Engineering Mechanics · doi:10.1016/j.probengmech.2023.103540 · Cited by 2

A Bayesian approach to online estimation of airgap spatial variation in induction machines with static eccentricity

Ardekani, I., & Nicholson, R. (2024) · Applied Mathematical Modelling · doi:10.1016/j.apm.2024.01.035 · Cited by 2

Likelihood-free inference and hierarchical data assimilation for geological carbon storage

Teng, W., & Durlofsky, L. J. (2025) · Advances in Water Resources · doi:10.1016/j.advwatres.2025.104961 · Cited by 2

Bayesian geo-additive modeling of zonal level crop production in Ethiopia

Mare, Y., Zewotir, T. T., & Belay, D. B. (2025) · Scientific African · doi:10.1016/j.sciaf.2025.e02813 · Cited by 3

Bayesian variable selection in Kriging metamodeling for quality design

Tao, B., Han, Z., Shi, W., Wang, M., & Ouyang, L. (2025) · European Journal of Operational Research · doi:10.1016/j.ejor.2025.06.003 · Cited by 2

A new perspective on precursors and rare events from a systematic review

Wen, H. (2025) · Journal of Loss Prevention in the Process Industries · doi:10.1016/j.jlp.2025.105785 · Cited by 2

Assessing intervention effects in a community-based trial to reduce self-harm: A methodological case study

Congdon, P. D., & Clarke, T. (2005) · Public Health · doi:10.1016/j.puhe.2005.01.017 · Cited by 2

Estimating Uncertainty in Reliability of Electricity Supply Analyses Considering Component Condition

Toftaker, H., Sperstad, I. B., & Kjølle, G. H. (2023) · European Safety and Reliability Conference (ESREL) · doi:10.3850/978-981-18-8071-1_p197-cd · Cited by 2

Modeling high-frequency financial data using R and Stan: A bayesian autoregressive conditional duration approach

Tabash, M. I., Navas, T. M., Thayyib, P. V., Farhin, S., Khan, A. A., & Hannoon, A. (2024) · Journal of Open Innovation Technology Market and Complexity · doi:10.1016/j.joitmc.2024.100249 · Cited by 2

Updating beliefs about variables given new information on how those variables relate

Bordley, R. F., & Bier, V. M. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2007.10.036 · Cited by 2

An application of document filtering in an operational system

Lehner, P., Worrell, C. A., Vu, C., Mittel, J. S., Snyder, S., Schulte, E., & Greiff, W. R. (2010) · Information Processing & Management · doi:10.1016/j.ipm.2009.12.006 · Cited by 2

Abstract Shape Aesthetics: Contour, Complexity, Motion, and Individual Variability

Soranzo, A., Bertacchini, F., & Bertamini, M. (2024) · Art & Perception · doi:10.1163/22134913-bja10057 · Cited by 2

Parallel block sparse Bayesian learning for high dimensional sparse signals

Boyle, O. R., Üney, M., Yi, X., & Brindley, J. (2025) · Signal Processing · doi:10.1016/j.sigpro.2025.109938 · Cited by 1

Quantifying hydrogen technology acceptance: Insights from Bayesian networks

Herr, D. Z., Scovell, M., Kinaev, N. N., & Vaisman, R. (2025) · Energy and Climate Change · doi:10.1016/j.egycc.2025.100201 · Cited by 1

Measuring cognitive load by a score-based causal network model with multichannel physiological signals

Liu, L., Pang, Q., Huang, S., Jiang, L., Wang, S., Wu, G., Su, G., & Tao, Q. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131290 · Cited by 1

Surrogate model for Bayesian optimal experimental design for adsorption isotherm parameters in chromatography

Rojo-Garcia, J. R., Haario, H., Helin, T., & Sainio, T. (2025) · Journal of Chromatography A · doi:10.1016/j.chroma.2025.466392 · Cited by 1

Uncertainty-aware prediction of chloride resistance in recycled aggregate concrete exposed to marine conditions

Zhang, Z., & Zhao, X. (2025) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2025.e05464 · Cited by 1

A latent variable model for estimating disease transmission rate from data on household outbreaks

Li, N., Qian, G., & Huggins, R. (2005) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2005.06.011 · Cited by 1

Approximate algorithms for neural-Bayesian approaches

Heskes, T., Bakker, B., & Kappen, B. (2002) · Theoretical Computer Science · doi:10.1016/s0304-3975(02)00132-9 · Cited by 1

Estimating Missing Values of WSN using modified Frequent itemsets mining and NN search

Luo, M., Wang, F., & Hu, X. (2015) · International Conference on Computer, Mechatronics, Control and Electronic Engineering · doi:10.2991/iccmcee-15.2015.120 · Cited by 1

Feasibility Study of Port Scan Detection on Encrypted Data

Chandrashekar, P., Dara, S., & Muralidhara, V. N. (2015) · IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) · doi:10.1109/ccem.2015.18 · Cited by 1

Large sample convergence diagnostics for likelihood based inference: Logistic regression

Brimacombe, M. B. (2016) · Statistical Methodology · doi:10.1016/j.stamet.2016.08.001 · Cited by 1

Marginal improvement procedures for top- m selection

Liu, H., Xing, Z., Xiao, H., & Chew, E. P. (2024) · Automatica · doi:10.1016/j.automatica.2024.111875 · Cited by 1

Improved Adaptive Filter with Unknown Process and Measurement Noise Covariance

Ma, J., Hu, Y., Ma, Q., Yang, S., Zheng, J., & Wang, S. (2020) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-8450-3_82 · Cited by 1

Statistical modelling for relative sea-level data

Upton, M., Cahill, N., & Parnell, A. C. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-99931-1.00025-8 · Cited by 1

An Iterated Conditional Modes solution for sparse Bayesian factor modeling of transcriptional regulatory networks

Meng, J., Zhang, J., Chen, Y., & Huang, Y. (2010) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2010.5706587

Bayesian Modeling of Spacecraft Safe Mode Events

Hooke, M., Chandler, G., & Imken, T. (2020) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero47225.2020.9172604

Bibliography

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Measuring the Uncertainty of Environmental Good Preferences with Bayesian Deep Learning

Flores, R., Tlachac, M., & Rundensteiner, E. A. (2022) · International Conference on Information Technology for Social Good (GoodIT) · doi:10.1145/3524458.3547250

Development and Bayesian calibration of a microsimulation model for lung cancer: natural history, screening and treatment

Halloluwage, E. H. N. (2023) · doi:10.26686/wgtn.24499828

Causal associations between scapular morphology and shoulder condition estimated with Bayesian statistics

Eghbali, P., Şatır, O. B., Becce, F., Goetti, P., Büchler, P., Pioletti, D. P., & Terrier, A. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108666

Evaluation of the cubicle hood sampler for monitoring methane production of dairy cows under barn conditions

Levrault, C. M., Koerkamp, P. W. G. G., Peeters, C. F. W., & Ogink, N. W. M. (2025) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2025.02.008

A Markov Chain Monte Carlo approach for complex lava flow simulations driven by satellite-derived data

Zuccarello, F., Bilotta, G., Cannavò, F., Cappello, A., Guardo, R., & Ganci, G. (2025) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2025.106426

Bayesian nonparametric estimation of differential entropy for toroidal data

Rad, N. N., Arashi, ‎., Bekker, A., & Millard, S. M. (2025) · Applied Mathematical Modelling · doi:10.1016/j.apm.2025.116241

Neural triangular map for density estimation and sampling with application to Bayesian inference

Wu, D., Chamoin, L., & Bressan, S. (2025) · Journal of Computational Physics · doi:10.1016/j.jcp.2025.114208

Delayed progressive apraxia of speech: A novel clinical entity distinct from primary progressive aphasia – A descriptive case series

Akaike, S., Ura, S., Ogata, A., Fukutake, T., Yabe, I., & Otsuki, M. (2025) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2025.123602

A distributed inference algorithm for Dirichlet process mixture models with exponential family components

Khoufache, R., Lebbah, M., Azzag, H., Goffinet, É., & Bouchaffra, D. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131119

Uncertainty quantification of flammable gas dispersion numerical models driven by hybrid variational inference deep learning

Xie, W., Wang, Q., Li, J., Xie, Z., Shi, J., Huang, X., & Usmani, A. S. (2025) · Journal of Loss Prevention in the Process Industries · doi:10.1016/j.jlp.2025.105758

Regenerating and estimating missing parameters in critical benchmark experiments: A framework for inverse uncertainty quantification using information-theoretic experiment design

Tian, X., Zhang, Q., & Zhang, G. (2025) · Progress in Nuclear Energy · doi:10.1016/j.pnucene.2025.106016

Non-Deterministic extension of plasma wind tunnel data calibrated model predictions to flight conditions

Rostkowski, P., Meurisse, J. B., & Panesi, M. (2025) · Aerospace Science and Technology · doi:10.1016/j.ast.2025.111059

Probabilistic evaluation of torsional resistance of high-performance and ultra-high-performance concrete beams: Bayesian approach

Zhang, Z., Jian, S.-M., Zang, J.-B., & Zhao, X. (2025) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2025.e05443

Bayesian Data Analysis Ed. 2

Gelman, A., Carlin, J. B., & Stern, H. S. (2003) · Taylor & Francis eBooks · Cited by 15

Bayesian methods for data analysis, third edition

Lu, Z. (2010) · Journal of Applied Statistics · doi:10.1080/02664760902811621 · Cited by 48

Computational Advances for and from Bayesian Analysis

Andrieu, C., Arnaud Doucet, B., & Robert, C. P. (2004) · Statistical Science · doi:10.1214/088342304000000071 · Cited by 62

Bayesian Statistical Methods

Reich, B. J., & Ghosh, S. K. (2019) · Chapman & Hall/CRC eBooks · doi:10.1201/9780429202292 · Cited by 59

Bayesian data analysis using R

Kerman, J., & Gelman, A. (2008)