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

Learning representations by back-propagating errors

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892

Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations

Rumelhart, D. E., McClelland, J. L., & AU (1986) · doi:10.7551/mitpress/5236.001.0001 · Cited by 15,686

The Theory of Probability

Jeffreys, H., Moran, P. A. P., & Chatfield, C. (1922) · Nature · doi:10.1038/109132a0 · Cited by 7,877

Statistical Decision Theory and Bayesian Analysis

Berger, J. O. (1985) · Springer Series in Statistics · doi:10.1007/978-1-4757-4286-2 · Cited by 7,722

Bayesian Interpolation

Mackay, D. (1992) · Neural Computation · doi:10.1162/neco.1992.4.3.415 · Cited by 4,440

Bayesian Learning for Neural Networks

Neal, R. M. (1996) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-0745-0 · Cited by 4,276

Bayesian Inference in Statistical Analysis.

Kadane, J. B., Box, G. E. P., & Tiao, G. C. (1975) · Journal of the American Statistical Association · doi:10.2307/2285412 · Cited by 3,853

Untitled work

(1989) · Cited by 3,214

A practical Bayesian framework for backpropagation networks

MacKay, D. J. C. (1992) · Neural Computation · doi:10.1162/neco.1992.4.3.448 · Cited by 2,917

Stastical Decision Theory and Bayesian Analysis.

Ghosh, M., & Berger, J. O. (1988) · Journal of the American Statistical Association · doi:10.2307/2288950 · Cited by 2,901

Bayesian Measures of Model Complexity and Fit

Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00353 · Cited by 151

Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?

Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., & Woolrich, M. W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.09.018 · Cited by 3,471

Supervised Sequence Labelling with Recurrent Neural Networks

Graves, A. (2012) · Studies in Computational Intelligence · doi:10.1007/978-3-642-24797-2 · Cited by 3,120

Dynamic bayesian networks: representation, inference and learning

Murphy, K. P., & Russell, S. (2002) · Cited by 2,579

Weight Uncertainty in Neural Network

Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D. (2015) · International Conference on Machine Learning · Cited by 1,335

Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses

Stegle, O., Parts, L., Piipari, M., Winn, J., & Durbin, R. (2012) · Nature Protocols · doi:10.1038/nprot.2011.457 · Cited by 1,049

Practical Variational Inference for Neural Networks

Graves, A. (2011) · Cited by 1,079

Mindless statistics.

Gigerenzer, G. (2004) · The Journal of Socio-Economics · doi:10.1016/j.socec.2004.09.033 · Cited by 890

A family of algorithms for approximate bayesian inference

Minka, T. P., & Picard, R. W. (2001) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 882

Variational free energy and the Laplace approximation

Friston, K., Mattout, J., Trujillo-Barreto, N., Ashburner, J., & Penny, W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.08.035 · Cited by 880

Benchmarking Least Squares Support Vector Machine Classifiers

Gestel, T. V., Suykens, J. A. K., Baesens, B., Viaene, S., Vanthienen, J., Dedene, G., Moor, B. D., & Vandewalle, J. P. L. (2003) · Machine Learning · doi:10.1023/b:mach.0000008082.80494.e0 · Cited by 737

The priority heuristic: Making choices without trade-offs.

Brandstätter, E., Gigerenzer, G., & Hertwig, R. (2006) · Psychological Review · doi:10.1037/0033-295x.113.2.409 · Cited by 770

Machine Learning for Survival Analysis

Wang, P., Li, Y., & Reddy, C. K. A. (2019) · ACM Computing Surveys · doi:10.1145/3214306 · Cited by 858

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

Jablonka, K. M., Ongari, D., Moosavi, S. M., & Smit, B. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00004 · Cited by 656

Probabilistic models of cognition: exploring representations and inductive biases

Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.05.004 · Cited by 568

Machine-learned potentials for next-generation matter simulations

Friederich, P., Häse, F., Proppe, J., & Aspuru‐Guzik, A. (2021) · Nature Materials · doi:10.1038/s41563-020-0777-6 · Cited by 595

Learning dynamic Bayesian networks

Ghahramani, Z. (1998) · Lecture notes in computer science · doi:10.1007/bfb0053999 · Cited by 561

Structural risk minimization over data-dependent hierarchies

Shawe-Taylor, J. S., Bartlett, P. L., Williamson, R. C., & Anthony, M. (1998) · IEEE Transactions on Information Theory · doi:10.1109/18.705570 · Cited by 522

Financial time series prediction using least squares support vector machines within the evidence framework

Gestel, T. V., Suykens, J. A. K., Baestaens, D.-E., Lambrechts, A., Lanckriet, G. R. G., Vandaele, B., Moor, B. D., & Vandewalle, J. P. L. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.935093 · Cited by 499

Automatic Choice of Dimensionality for PCA

Minka, T. P. (2000) · Cited by 499

A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies

Stegle, O., Parts, L., Durbin, R., & Winn, J. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000770 · Cited by 497

Bayesian Neural Networks for Internet Traffic Classification

Auld, T., Moore, A. W., & Gull, S. F. (2007) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.883010 · Cited by 494

Constructive algorithms for structure learning in feedforward neural networks for regression problems

Kwok, T.-Y., & Yeung, D. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.572102 · Cited by 490

Variational Bayesian Inference for a Nonlinear Forward Model

Chappell, M. A., Groves, A. R., Whitcher, B. J., & Woolrich, M. W. (2008) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2008.2005752 · Cited by 468

Unsupervised Learning

Ghahramani, Z. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-28650-9_5 · Cited by 476

Gradient-Based Optimization of Hyperparameters

Bengio, Y. (2000) · Neural Computation · doi:10.1162/089976600300015187 · Cited by 467

A review of evidence of health benefit from artificial neural networks in medical intervention

Lisboa, P. J. G. (2002) · Neural Networks · doi:10.1016/s0893-6080(01)00111-3 · Cited by 463

The Variational Bayesian EM Algorithm for Incomplete Data: With Application to Scoring Graphical Model Structures

Beal, M. J., & Ghahramani, Z. (2003) · Bayesian Statistics 7 · doi:10.1093/oso/9780198526155.003.0025 · Cited by 458

Nonrigid Structure-from-Motion: Estimating Shape and Motion with Hierarchical Priors

Torresani, L., Hertzmann, A., & Bregler, C. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70752 · Cited by 456

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

Psaros, A. F., Meng, X., Zou, Z., Guo, L., & Karniadakis, G. E. (2023) · Journal of Computational Physics · doi:10.1016/j.jcp.2022.111902 · Cited by 464

Accurate freeway travel time prediction with state-space neural networks under missing data

van Lint, H., Hoogendoorn, S. P., & van Zuylen, H. J. (2005) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2005.03.001 · Cited by 448

Neural networks in business: a survey of applications (1992–1998)

Vellido, A. (1999) · Expert Systems with Applications · doi:10.1016/s0957-4174(99)00016-0 · Cited by 428

The graphical brain: Belief propagation and active inference

Friston, K. J., Parr, T., & de Vries, B. (2017) · Network Neuroscience · doi:10.1162/netn_a_00018 · Cited by 415

Dynamic modeling of genetic networks using genetic algorithm and S-system

Kikuchi, S., Tominaga, D., Arita, M., Takahashi, K., & Tomita, M. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg027 · Cited by 401

Overlapping community detection using Bayesian non-negative matrix factorization

Psorakis, I., Roberts, S., Ebden, M., & Sheldon, B. C. (2011) · Physical Review E · doi:10.1103/physreve.83.066114 · Cited by 399

Language Evolution by Iterated Learning With Bayesian Agents

Griffiths, T. L., & Kalish, M. L. (2007) · Cognitive Science · doi:10.1080/15326900701326576 · Cited by 404

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

Analytical estimates of limited sampling biases in different information measures

Panzeri, S., & Treves, A. (1996) · Network: Computation in Neural Systems · doi:10.1080/0954898x.1996.11978656 · Cited by 138

Modelling Gene Expression Data using Dynamic Bayesian Networks

Murphy, K. J. (2006) · Cited by 353

Bayesian Compression for Deep LearningPreprint

Louizos, C., Ullrich, K., & Welling, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.08665 · Cited by 375

Bayesian Deep Learning and a Probabilistic Perspective of GeneralizationPreprint

Wilson, A. G., & Izmailov, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.08791 · Cited by 428

Blind image deconvolution: theory and applications

Campisi, P., & Egiazarian, K. (2007) · Cited by 348

Variational Bayesian Model Selection for Mixture Distributions

Corduneanu, A., & Bishop, C. (2001) · Cited by 345

COMPLETE FUNCTIONAL CHARACTERIZATION OF SENSORY NEURONS BY SYSTEM IDENTIFICATION

Wu, M. C., David, S. V., & Gallant, J. L. (2006) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.113024 · Cited by 339

A tutorial on support vector machine-based methods for classification problems in chemometrics

Luts, J., Ojeda, F., de Plas, R. V., Moor, B. D., Huffel, S. V., & Suykens, J. A. K. (2010) · Analytica Chimica Acta · doi:10.1016/j.aca.2010.03.030 · Cited by 336

Soft sensing modeling based on support vector machine and Bayesian model selection

Yan, W., Shao, H., & Wang, X. (2004) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2003.11.004 · Cited by 326

Estimation of bainite plate-thickness in low-alloy steels

Singh, S. B., & Bhadeshia, H. K. D. H. (1998) · Materials Science and Engineering A · doi:10.1016/s0921-5093(97)00701-6 · Cited by 320

Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images

Chu, C., Hsu, A.-L., Chou, K.-H., Bandettini, P., & Lin, C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.066 · Cited by 319

Towards dropout training for convolutional neural networks

Wu, H., & Gu, X. (2015) · Neural Networks · doi:10.1016/j.neunet.2015.07.007 · Cited by 329

A small number of abnormal brain connections predicts adult autism spectrum disorder

Yahata, N., Morimoto, J., Hashimoto, R., Lisi, G., Shibata, K., Kawakubo, Y., Kuwabara, H., Kuroda, M., Yamada, T., Megumi, F., et al. (2016) · Nature Communications · doi:10.1038/ncomms11254 · Cited by 305

A tutorial introduction to Bayesian models of cognitive development

Perfors, A., Tenenbaum, J. B., Griffiths, T. L., & Xu, F. (2011) · Cognition · doi:10.1016/j.cognition.2010.11.015 · Cited by 300

Structural learning with forgetting

Ishikawa, M. (1996) · Neural Networks · doi:10.1016/0893-6080(96)83696-3 · Cited by 300

Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis

Gestel, T. V., Suykens, J. A. K., Lanckriet, G. R. G., Lambrechts, A., Moor, B. D., & Vandewalle, J. P. L. (2002) · Neural Computation · doi:10.1162/089976602753633411 · Cited by 291

Natural computing for mechanical systems research: A tutorial overview

Worden, K., Staszewski, W. J., & Hensman, J. J. (2010) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2010.07.013 · Cited by 297

Waking and dreaming consciousness: neurobiological and functional considerations.

Hobson, J. A., & Friston, K. J. (2012) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2012.05.003 · Cited by 287

Network discovery with DCM

Friston, K. J., Li, B., Daunizeau, J., & Stephan, K. E. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.039 · Cited by 283

A pre-trained convolutional neural network based method for thyroid nodule diagnosis

Ma, J., Wu, F., Zhu, J., Xu, D. M., & Kong, D. (2016) · Ultrasonics · doi:10.1016/j.ultras.2016.09.011 · Cited by 273

Automatic Relevance Determination in Nonnegative Matrix Factorization with the /spl beta/-Divergence

Tan, V. Y. F., & Févotte, C. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.240 · Cited by 271

Design of inferential sensors in the process industry: A review of Bayesian methods

Khatibisepehr, S., Huang, B., & Khare, S. (2013) · Journal of Process Control · doi:10.1016/j.jprocont.2013.05.007 · Cited by 261

Predicting clinical scores from magnetic resonance scans in Alzheimer's disease

Stonnington, C. M., Chu, C., Klöppel, S., Jack, C. R., Ashburner, J., & Frackowiak, R. S. J. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.051 · Cited by 263

Machine Theory of Mind

Rabinowitz, N. C., Perbet, F., Song, H. F., Zhang, C., Eslami, S. M. A., & Botvinick, M. (2018) · arXiv · doi:10.48550/arxiv.1802.07740

Fully Bayesian Spatio-Temporal Modeling of FMRI Data

Woolrich, M. W., Jenkinson, M., Brady, J. M., & Smith, S. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.823065 · Cited by 248

Bayesian Neural Networks: An Introduction and Survey

Goan, E., & Fookes, C. (2020) · Lecture notes in mathematics · doi:10.1007/978-3-030-42553-1_3 · Cited by 280

An Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables and Nutrient Concentrations From T, S, and O2 Data Using Bayesian Neural Networks

Bittig, H. C., Steinhoff, T., Claustre, H., Fiedler, B., Williams, N. L., Sauzède, R., Körtzinger, A., & Gattuso, J. (2018) · Frontiers in Marine Science · doi:10.3389/fmars.2018.00328 · Cited by 260

The Principles of Deep Learning Theory

Roberts, D. A., Yaida, S., & Hanin, B. (2022) · Cambridge University Press eBooks · doi:10.1017/9781009023405 · Cited by 349

Variational Gaussian process classifiers

Mackay, D., & Gibbs, M. (2000) · IEEE Transactions on Neural Networks · doi:10.1109/72.883477 · Cited by 225

Bayesian PCA

Bishop, C. (1998) · neural information processing systems · Cited by 224

Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery

Pyzer‐Knapp, E. O., Li, K., & Aspuru‐Guzik, A. (2015) · Advanced Functional Materials · doi:10.1002/adfm.201501919 · Cited by 220

Analytical estimates of limited sampling biases in different information measures

Panzeri, S., & Treves, A. (1996) · Network Computation in Neural Systems · doi:10.1088/0954-898x/7/1/006 · Cited by 207

Online Learning Solutions for Freeway Travel Time Prediction

van Lint, H. (2008) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2008.915649 · Cited by 200

How the Bayesians got their beliefs (and what those beliefs actually are): Comment on Bowers and Davis (2012).

Griffiths, T. L., Chater, N., Norris, D., & Pouget, A. (2012) · Psychological Bulletin · doi:10.1037/a0026884 · Cited by 192

Automatic recognition of alertness and drowsiness from EEG by an artificial neural network

Vučković, A., Radivojević, V., Chen, A. C. N., & Popović, D. B. (2002) · Medical Engineering & Physics · doi:10.1016/s1350-4533(02)00030-9 · Cited by 201

Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland

Deo, R. C., & Şahin, M. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.01.114 · Cited by 194

Intrinsic dimension estimation: Advances and open problems

Camastra, F., & Staiano, A. (2015) · Information Sciences · doi:10.1016/j.ins.2015.08.029 · Cited by 204

Artificial neural networks and genetic algorithms in QSAR

Niculescu, Ş. P. (2003) · Journal of Molecular Structure THEOCHEM · doi:10.1016/s0166-1280(02)00619-x · Cited by 197

Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks

Ma, J., Wu, F., Jiang, T., Zhao, Q., & Kong, D. (2017) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-017-1649-7 · Cited by 187

An introduction to artificial neural networks in bioinformatics--application to complex microarray and mass spectrometry datasets in cancer studies

Lancashire, L., Lemetre, C., & Ball, G. (2008) · Briefings in Bioinformatics · doi:10.1093/bib/bbp012 · Cited by 186

Bayesian kernel based classification for financial distress detection

Gestel, T. V., Baesens, B., Suykens, J. A. K., den Poel, D. V., Baestaens, D.-E., & Willekens, M. (2005) · European Journal of Operational Research · doi:10.1016/j.ejor.2004.11.009 · Cited by 187

Low-Rank Modeling and Its Applications in Image Analysis

Zhou, X., Yang, C., Zhao, H., & Yu, W. (2014) · ACM Computing Surveys · doi:10.1145/2674559 · Cited by 174

A PAC analysis of a Bayesian estimator

Shawe-Taylor, J. S., & Williamson, R. C. (1997) · Conference on Learning Theory · doi:10.1145/267460.267466 · Cited by 195

Constructing Site-Specific Multivariate Probability Distribution Model Using Bayesian Machine Learning

Ching, J., & Phoon, K. (2018) · Journal of Engineering Mechanics · doi:10.1061/(asce)em.1943-7889.0001537 · Cited by 165

BELM: Bayesian Extreme Learning Machine

Soria‐Olivas, E., Gómez‐Sanchís, J., Martín‐Guerrero, J. D., Vila‐Francés, J., Martinez, M., Magdalena‐Benedito, R., & Serrano-López, A. J. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2103956 · Cited by 164

Bayesian Methods for Neural Networks and Related Models

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

Safe Visual Navigation via Deep Learning and Novelty Detection

Richter, C., & Roy, N. (2017) · Robotics: Science and Systems · doi:10.15607/rss.2017.xiii.064 · Cited by 160

Bayesian learning for neural networks: an algorithmic survey

Magris, M., & Iosifidis, A. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10443-1 · Cited by 182

Ensemble learning in Bayesian neural networks

Barber, D., & Bishop, C. (1998) · Cited by 166

Moderating the outputs of support vector machine classifiers

Kwok, J. T.-Y. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.788642 · Cited by 155

Bayesian neural networks: An introduction and survey

Goan, E., & Fookes, C. (2020) · QUT ePrints (Queensland University of Technology) · Cited by 162

Revisiting Stacking Fault Energy of Steels

Das, A. (2015) · Metallurgical and Materials Transactions A · doi:10.1007/s11661-015-3266-9 · Cited by 155

DropConnect is effective in modeling uncertainty of Bayesian deep networks

Mobiny, A., Yuan, P., Moulik, S. K., Garg, N., Wu, C. C., & Nguyen, H. V. (2021) · Scientific Reports · doi:10.1038/s41598-021-84854-x · Cited by 183

Bayesian committee of neural networks to predict travel times with confidence intervals

van Hinsbergen, C. P. I., van Lint, H., & van Zuylen, H. J. (2009) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2009.04.007 · Cited by 150

Probabilistic neural networks for fluid flow surrogate modeling and data recovery

Maulik, R., Fukami, K., Ramachandra, N., Fukagata, K., & Taira, K. (2020) · Physical Review Fluids · doi:10.1103/physrevfluids.5.104401 · Cited by 153

A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer

Lisboa, P. J. G., Wong, H., Harris, P., & Swindell, R. (2003) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(03)00033-2 · Cited by 149

Bayesian Support Vector Regression Using a Unified Loss Function

Chu, W., Keerthi, S. S., & Ong, C. J. (2004) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2003.820830 · Cited by 144

Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models

Deo, R. C., Samui, P., & Kim, D. (2015) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-015-1153-y · Cited by 142

Super Resolution of Images and Video

Katsaggelos, A. K., Molina, R., & Javier Mateos, A. (2007) · Synthesis lectures on image, video, and multimedia processing · doi:10.1007/978-3-031-02243-2 · Cited by 142

Bayesian Gaussian Process Models: PAC-Bayesian Generalisation Error Bounds and Sparse Approximations

Seeger, M. (2003) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 143

Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images

Ma, J., Wu, F., Jiang, T., Zhu, J., & Kong, D. (2017) · Medical Physics · doi:10.1002/mp.12134 · Cited by 142

Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference

Vong, C., Wong, P.-K., & Li, Y. (2005) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2005.09.001 · Cited by 139

Learning Non-Rigid 3D Shape from 2D Motion

Torresani, L., Hertzmann, A., & Bregler, C. (2003) · Cited by 139

Bayesian Methods for Backpropagation Networks

Mackay, D. (1996) · Physics of neural networks · doi:10.1007/978-1-4612-0723-8_6 · Cited by 229

Bayesian modelling of neural networks

Mutihac, R., Cicuttin, A., Estrada, A. C., & Colavita, A. (1999) · Lecture notes in computer science · doi:10.1007/bfb0098183

Bayesian Techniques

Bishop, C. (1995) · doi:10.1093/oso/9780198538493.003.0010 · Cited by 4

Bayesian Methods for Supervised Neural Networks

Barber, D. (2002)

Bayesian methods for neural networks

de Freitas, G., & Ferdinando, J. (2000) · Cited by 81