Statistical Decision Theory and Bayesian Analysis

Bayes factors

Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558 · see citing works

Bidirectional recurrent neural networks

Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172 · see citing works

Generalized additive models for location, scale and shape.

Rigby, R. A., & Stasinopoulos, D. M. (2005) · Journal of the Royal Statistical Society Series C: Applied Statistics · doi:10.1111/j.1467-9876.2005.00510.x · Cited by 3,228 · see citing works

Genomic Control for Association Studies

Devlin, B., & Roeder, K. (1999) · Biometrics · doi:10.1111/j.0006-341x.1999.00997.x · Cited by 3,162 · see citing works

The trouble with overconfidence.

Moore, D. A., & Healy, P. J. (2008) · Psychological Review · doi:10.1037/0033-295x.115.2.502 · Cited by 2,358 · see citing works

The use of the multi-model ensemble in probabilistic climate projections

Tebaldi, C., & Knutti, R. (2007) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2007.2076 · Cited by 1,862 · see citing works

Object perception as Bayesian inference

Kersten, D., Mamassian, P., & Yuille, A. (2004) · Annual Review of Psychology · doi:10.1146/annurev.psych.55.090902.142005 · Cited by 1,269 · see citing works

The adaptive nature of human categorization.

Anderson, J. R. (1991) · Psychological Review · doi:10.1037/0033-295x.98.3.409 · Cited by 1,069 · see citing works

Kernel methods in system identification, machine learning and function estimation: A survey

Pillonetto, G., Dinuzzo, F., Chen, T., De Nicolao, G., & Ljung, L. (2014) · Automatica · doi:10.1016/j.automatica.2014.01.001 · Cited by 741 · see citing works

Testing Precise Hypotheses

Berger, J. O., & Delampady, M. (1987) · Statistical Science · doi:10.1214/ss/1177013238 · Cited by 664 · see citing works

Algorithmic Fairness: Choices, Assumptions, and Definitions

Mitchell, S., Potash, E., Barocas, S., D'Amour, A., & Lum, K. (2021) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-042720-125902 · Cited by 687 · see citing works

A model of inductive bias learning

Baxter, J. (2000) · Journal of Artificial Intelligence Research · doi:10.1613/jair.731 · Cited by 582 · see citing works

Decision theory, reinforcement learning, and the brain

Dayan, P., & Daw, N. D. (2008) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/cabn.8.4.429 · Cited by 580 · see citing works

Doing Bayesian Data Analysis: A Tutorial with R and BUGS

Barry, J. (2011) · Europe’s Journal of Psychology · doi:10.5964/ejop.v7i4.163 · Cited by 563 · see citing works

The interplay of Bayesian and frequentist analysis

Bayarri, M. J., & Berger, J. O. (2004) · Statistical Science · doi:10.1214/088342304000000116 · Cited by 525 · see citing works

An introduction to Bayesian inference for ecological research and environmental decision-making

Ellison, A. M. (1996) · Ecological Applications · doi:10.2307/2269588 · Cited by 482 · see citing works

Simulation-based optimal bayesian experimental design for nonlinear systems

Huan, X., & Marzouk, Y. M. (2013) · Journal of Computational Physics · doi:10.1016/j.jcp.2012.08.013 · Cited by 438 · see citing works

The Effect of Improper Priors on Gibbs Sampling in Hierarchical Linear Mixed Models

Hobert, J. P., & Casella, G. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476714 · Cited by 435 · see citing works

Bayesian approach for neural networks—review and case studies

Lampinen, J., & Vehtari, A. (2001) · Neural Networks · doi:10.1016/s0893-6080(00)00098-8 · Cited by 392 · see citing works

Suboptimality in perceptual decision making

Rahnev, D., & Denison, R. N. (2018) · Behavioral and Brain Sciences · doi:10.1017/s0140525x18000936 · Cited by 342 · see citing works

Scientific Method in Practice

Gauch Jr, H. G. (2002) · doi:10.1017/cbo9780511815034 · Cited by 385 · see citing works

On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection

Lai, V., & Tan, C. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287590 · Cited by 377 · see citing works

An exploration of aspects of Bayesian multiple testing

Scott, J. G., & Berger, J. O. (2006) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2005.08.031 · Cited by 330 · see citing works

THE EVOLVING CONTEXT OF THE FUNDAMENTAL DIFFERENCE HYPOTHESIS

Bley-Vroman, R. (2009) · Studies in Second Language Acquisition · doi:10.1017/s0272263109090275 · Cited by 327 · see citing works

Probable networks and plausible predictions — a review of practical Bayesian methods for supervised neural networks

Mackay, D. J. C. (1995) · Network Computation in Neural Systems · doi:10.1088/0954-898x/6/3/011 · Cited by 915 · see citing works

DEM: A variational treatment of dynamic systems

Friston, K. J., Trujillo-Barreto, N., & Daunizeau, J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.02.054 · Cited by 278 · see citing works

Bayesian Random-Effects Meta-Analysis Using the <b>bayesmeta</b> <i>R</i> Package

Röver, C. (2020) · Journal of Statistical Software · doi:10.18637/jss.v093.i06 · Cited by 310 · see citing works

When Null Hypothesis Significance Testing Is Unsuitable for Research: A Reassessment

Szucs, D., & Ioannidis, J. P. A. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00390 · Cited by 298 · see citing works

Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings

Payzan-LeNestour, E., & Bossaerts, P. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001048 · Cited by 267 · see citing works

What is a statistical model?

McCullagh, P. (2002) · The Annals of Statistics · doi:10.1214/aos/1035844977 · Cited by 248 · see citing works

Active inference on discrete state-spaces: A synthesis

Da Costa, L., Parr, T., Sajid, N., Veselic, S., Neacsu, V., & Friston, K. (2020) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2020.102447 · Cited by 226 · see citing works

Branch-length prior influences Bayesian posterior probability of phylogeny.

Yang, Z., & Rannala, B. (2005) · Systematic Biology · doi:10.1080/10635150590945313 · Cited by 228 · see citing works

Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data

Kim, S., Imoto, S., & Miyano, S. (2004) · Biosystems · doi:10.1016/j.biosystems.2004.03.004 · Cited by 224 · see citing works

Dynamic Online Pricing with Incomplete Information Using Multiarmed Bandit Experiments

Misra, K., Schwartz, E. M., & Abernethy, J. (2019) · Marketing Science · doi:10.1287/mksc.2018.1129 · Cited by 183 · see citing works

Bayesian Modelling of Visual Perception

Mamassian, P., Landy, M., & Maloney, L. T. (2002) · Probabilistic Models of the Brain · doi:10.7551/mitpress/5583.003.0005 · Cited by 210 · see citing works

The free energy principle made simpler but not too simple

Friston, K., Da Costa, L., Sajid, N., Heins, C., Ueltzhöffer, K., Pavliotis, G. A., & Parr, T. (2023) · Physics Reports · doi:10.1016/j.physrep.2023.07.001 · Cited by 163 · see citing works

Meta‐analysis for 2 × 2 tables: A bayesian approach

Carlin, J. B. (1992) · Statistics in Medicine · doi:10.1002/sim.4780110202 · Cited by 164 · see citing works

Bayesian Methods for Neural Networks and Related Models

Titterington, D. M. (2004) · Statistical Science · doi:10.1214/088342304000000099 · Cited by 162 · see citing works

Cue combination in human spatial navigation

Chen, X., McNamara, T. P., Kelly, J. W., & Wolbers, T. (2017) · Cognitive Psychology · doi:10.1016/j.cogpsych.2017.04.003 · Cited by 122 · see citing works

Linkage between Financial Inclusion and Economic Growth: An Empirical Study of the Emerging Indian Economy

Dahiya, S., & Kumar, M. (2020) · Vision: The Journal of Business Perspective · doi:10.1177/0972262920923891 · Cited by 129 · see citing works

Survey and analysis of hallucinations in large language models: attribution to prompting strategies or model behavior

Anh-Hoang, D., Tran, V., & Nguyen, L.-M. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1622292 · Cited by 146 · see citing works

Bayesian Meta-Analysis with Weakly Informative Prior Distributions

Williams, D. R., Rast, P., & Bürkner, P. C. (2018) · doi:10.31234/osf.io/7tbrm · Cited by 124 · see citing works

Bayesian natural selection and the evolution of perceptual systems

Geisler, W. S., & Diehl, R. L. (2002) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.2001.1055 · Cited by 101 · see citing works

A Bayesian Perspective on Sensory and Cognitive Integration in Pain Perception and Placebo Analgesia

Anchisi, D., & Zanon, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0117270 · Cited by 84 · see citing works

Generating meaning: active inference and the scope and limits of passive AI

Pezzulo, G., Parr, T., Cisek, P., Clark, A., & Friston, K. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.10.002 · Cited by 80 · see citing works

Implementation of Dynamic Bayesian Decision Making by Intracellular Kinetics

Kobayashi, T. J. (2010) · Physical Review Letters · doi:10.1103/physrevlett.104.228104 · Cited by 68 · see citing works

Charting the landscape of priority problems in psychiatry, part 2: pathogenesis and aetiology

Stephan, K. E., Binder, E. B., Breakspear, M., Dayan, P., Johnstone, E. C., Meyer-Lindenberg, A., Schnyder, U., Wang, X.-J., Bach, D. R., Fletcher, P. C., et al. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(15)00360-0 · Cited by 64 · see citing works

The promises and pitfalls of applying computational models to neurological and psychiatric disorders

Teufel, C., & Fletcher, P. C. (2016) · Brain · doi:10.1093/brain/aww209 · Cited by 64 · see citing works

A simulation-intensive approach for checking hierarchical models

Dey, D. K., Gelfand, A. E., Swartz, T. B., & Vlachos, P. K. (1998) · Test · doi:10.1007/bf02565116 · Cited by 58 · see citing works

Basic Framework and Main Methods of Uncertainty Quantification

Zhang, J., Yin, J., & Wang, R. (2020) · Mathematical Problems in Engineering · doi:10.1155/2020/6068203 · Cited by 52 · see citing works

An artificial intelligence tool to predict fluid requirement in the intensive care unit: a proof-of-concept study

Celi, L. A., Hinske Christian, L., Alterovitz, G., & Szolovits, P. (2008) · Critical Care · doi:10.1186/cc7140 · Cited by 52 · see citing works

Conservation of harvested populations in fluctuating environments - the case of the serengeti wildebeest.

Pascual, M. A., & Hilborn, R. (1995) · The Journal of Applied Ecology · doi:10.2307/2404645 · Cited by 41 · see citing works

Reward maximization through discrete active inference

Da Costa, L., Sajid, N., Parr, T., Friston, K., & Smith, R. (2023) · Neural Computation · doi:10.1162/neco_a_01574 · Cited by 35 · see citing works

Interacting Agricultural Pests and Their Effect on Crop Yield: Application of a Bayesian Decision Theory Approach to the Joint Management of Bromus tectorum and Cephus cinctus

Keren, I. N., Menalled, F. D., Weaver, D. K., & Robison-Cox, J. F. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118111 · Cited by 25 · see citing works

Bayesian Thinking about Macrosociology

Western, B. (2001) · American Journal of Sociology · doi:10.1086/323639 · Cited by 26 · see citing works

Cultivating creativity: predictive brains and the enlightened room problem

Constant, A., Friston, K. J., & Clark, A. (2024) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2022.0415 · Cited by 19 · see citing works

Using statistical methods to model the fine-tuning of molecular machines and systems

Thorvaldsen, S., & Hössjer, O. (2020) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2020.110352 · Cited by 15 · see citing works

Quantum formalism for the dynamics of cognitive psychology

Brody, D. C. (2023) · Scientific Reports · doi:10.1038/s41598-023-43403-4 · Cited by 13 · see citing works

Is cosmological tuning fine or coarse?

Díaz-Pachón, D. A., Hössjer, O., & Marks II, R. J. (2021) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2021/07/020 · Cited by 10 · see citing works

The potential for meta-analysis to support decision analysis in ecology: Meta-analysis and decision analysis

Mengersen, K., MacNeil, M. A., & Caley, M. J. (2015) · Research Synthesis Methods · doi:10.1002/jrsm.1105 · Cited by 10 · see citing works

Generating Meaning: Active Inference and the Scope and Limits of Passive AI

Pezzulo, G., Parr, T., Cisek, P., Clark, A., & Friston, K. (2023) · doi:10.31234/osf.io/8xgzv · Cited by 8 · see citing works