Meta-Learning for Bayesian Computation in Survival Analysis: A Machine Learning Framework for Intelligent Bayesian Algorithm Recommendation

Khalid Salah; Afnan Salah

doi:doi:10.11648/j.ajtas.20261505.16

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Meta-Learning for Bayesian Computation in Survival Analysis: A Machine Learning Framework for Intelligent Bayesian Algorithm Recommendation

Khalid Salah^*

, Afnan Salah

Published in American Journal of Theoretical and Applied Statistics (Volume 15, Issue 5)

Received: 21 August 2026 Accepted: 31 August 2026 Published: 22 September 2026

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Abstract

Bayesian computation for survival analysis often involves selecting among alternative Markov chain Monte Carlo (MCMC) algorithms whose performance can vary substantially with the characteristics of the data and model. In practice, this selection is frequently based on trial and error, requiring repeated model fitting and computational assessment. We propose a meta-learning framework that formulates Bayesian computational strategy selection as a data-dependent prediction problem. A simulation-based meta-database is constructed by generating survival datasets under systematically varied sample sizes, model dimensions, predictor correlations, and censoring proportions. Four Bayesian computational strategies—Standard Random-Walk MCMC, Adaptive MCMC, Hamiltonian Monte Carlo, and Reversible Jump MCMC—are evaluated across the simulated environments. Estimation accuracy, sampling efficiency, and computational cost are integrated through a multi-criteria utility function to identify the preferred computational strategy for each dataset. Machine-learning models are then trained to learn the relationship between survival-data characteristics and the resulting algorithm-selection decisions. The learned recommendation models are evaluated using three independent real survival datasets: the German Breast Cancer Study Group breast cancer, Veteran lung cancer, and primary biliary cirrhosis datasets. Importantly, the real-data evaluation is conducted without retraining or recalibration of the meta-learning models. The proposed framework provides a systematic approach for transferring computational experience from simulated survival environments to new problems. By treating Bayesian algorithm selection as a data-dependent decision rather than assuming a universally optimal MCMC strategy, the framework offers a practical approach for reducing trial-and-error computation and supporting evidence-based selection of Bayesian computational methods.

Published in	American Journal of Theoretical and Applied Statistics (Volume 15, Issue 5)
DOI	10.11648/j.ajtas.20261505.16
Page(s)	257-275
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Bayesian Computation, Survival Analysis, Meta-learning, Algorithm Selection, Markov Chain Monte Carlo, Adaptive MCMC, Hamiltonian Monte Carlo, Reversible Jump MCMC

References

[1]	Alvares D, Lázaro E, Gómez-Rubio V, Armero C. Bayesian survival analysis with BUGS. Statistics in Medicine. 2021; 40(12): 2928–2946. https://doi.org/10.1002/sim.8933
[2]	Bartoš F, Aust F, Haaf JM. Informed Bayesian survival analysis. BMC Medical Research Methodology. 2022; 22: 238. https://doi.org/10.1186/s12874-022-01676-9
[3]	Baratchi, M., Wang, C., Limmer, S., van Rijn, J. N., Hoos, H. H., Bäck, T., and M. Olhofer. 2024. Automated machine learning: Past, present and future. Artificial Intelligence Review 57: 122.
[4]	Brazdil P, van Rijn JN, Soares C, Vanschoren J. Metalearning: Applications to Automated Machine Learning and Data Mining. 2nd ed. Cham: Springer; 2022. https://doi.org/10.1007/978-3-030-67024-5
[5]	Dagan, I., Vainshtein, R., Katz, G., and L. Rokach. 2024. Automated algorithm selection using meta-learning and pre-trained deep convolution neural networks. Information Fusion 105: 102210.
[6]	He, X., Zhao, K., and X. Chu. 2021. AutoML: A survey of the state-of-the-art. Knowledge-Based Systems 212: 106622.
[7]	Martin, G. M., Frazier, D. T., and C. P. Robert. 2024. Computing Bayes: From then ’til now. Statistical Science 39: 3-19.
[8]	Nguyen, M. H., Sun-Hosoya, L., and I. Guyon. 2024. Meta-learning from learning curves for budget-limited algorithm selection. Pattern Recognition Letters 185: 225-231.
[9]	Navarro JM, Huet A, Rossi D. Meta-learning for fast model recommendation in unsupervised multivariate time series anomaly detection. Proceedings of Machine Learning Research. 2023; 224: 24/1–24/19.
[10]	Štrumbelj, E., Bouchard-Côté, A., Corander, J., Gelman, A., Rue, H., Murray, L., Pesonen, H., Plummer, M., and A. Vehtari. 2024. Past, present and future of software for Bayesian inference. Statistical Science 39: 46-61.
[11]	Sun Y, Song Q, Gui X, Ma F, Wang T. AutoML in the wild: obstacles, workarounds, and expectations. In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. New York: Association for Computing Machinery; 2023. Article 247. https://doi.org/10.1145/3544548.3581082
[12]	Winter S, Campbell T, Lin L, Srivastava S, Dunson DB. Emerging directions in Bayesian computation. Statistical Science. 2024; 39(1): 62–89. https://doi.org/10.1214/23-STS919

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APA Style

Salah, K., Salah, A. (2026). Meta-Learning for Bayesian Computation in Survival Analysis: A Machine Learning Framework for Intelligent Bayesian Algorithm Recommendation. American Journal of Theoretical and Applied Statistics, 15(5), 257-275. https://doi.org/10.11648/j.ajtas.20261505.16

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ACS Style

Salah, K.; Salah, A. Meta-Learning for Bayesian Computation in Survival Analysis: A Machine Learning Framework for Intelligent Bayesian Algorithm Recommendation. Am. J. Theor. Appl. Stat. 2026, 15(5), 257-275. doi: 10.11648/j.ajtas.20261505.16

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AMA Style

Salah K, Salah A. Meta-Learning for Bayesian Computation in Survival Analysis: A Machine Learning Framework for Intelligent Bayesian Algorithm Recommendation. Am J Theor Appl Stat. 2026;15(5):257-275. doi: 10.11648/j.ajtas.20261505.16

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@article{10.11648/j.ajtas.20261505.16,
  author = {Khalid Salah and Afnan Salah},
  title = {Meta-Learning for Bayesian Computation in Survival Analysis: A Machine Learning Framework for Intelligent Bayesian Algorithm Recommendation},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {15},
  number = {5},
  pages = {257-275},
  doi = {10.11648/j.ajtas.20261505.16},
  url = {https://doi.org/10.11648/j.ajtas.20261505.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20261505.16},
  abstract = {Bayesian computation for survival analysis often involves selecting among alternative Markov chain Monte Carlo (MCMC) algorithms whose performance can vary substantially with the characteristics of the data and model. In practice, this selection is frequently based on trial and error, requiring repeated model fitting and computational assessment. We propose a meta-learning framework that formulates Bayesian computational strategy selection as a data-dependent prediction problem. A simulation-based meta-database is constructed by generating survival datasets under systematically varied sample sizes, model dimensions, predictor correlations, and censoring proportions. Four Bayesian computational strategies—Standard Random-Walk MCMC, Adaptive MCMC, Hamiltonian Monte Carlo, and Reversible Jump MCMC—are evaluated across the simulated environments. Estimation accuracy, sampling efficiency, and computational cost are integrated through a multi-criteria utility function to identify the preferred computational strategy for each dataset. Machine-learning models are then trained to learn the relationship between survival-data characteristics and the resulting algorithm-selection decisions. The learned recommendation models are evaluated using three independent real survival datasets: the German Breast Cancer Study Group breast cancer, Veteran lung cancer, and primary biliary cirrhosis datasets. Importantly, the real-data evaluation is conducted without retraining or recalibration of the meta-learning models. The proposed framework provides a systematic approach for transferring computational experience from simulated survival environments to new problems. By treating Bayesian algorithm selection as a data-dependent decision rather than assuming a universally optimal MCMC strategy, the framework offers a practical approach for reducing trial-and-error computation and supporting evidence-based selection of Bayesian computational methods.},
 year = {2026}
}

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TY  - JOUR
T1  - Meta-Learning for Bayesian Computation in Survival Analysis: A Machine Learning Framework for Intelligent Bayesian Algorithm Recommendation
AU  - Khalid Salah
AU  - Afnan Salah
Y1  - 2026/09/22
PY  - 2026
N1  - https://doi.org/10.11648/j.ajtas.20261505.16
DO  - 10.11648/j.ajtas.20261505.16
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 257
EP  - 275
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20261505.16
AB  - Bayesian computation for survival analysis often involves selecting among alternative Markov chain Monte Carlo (MCMC) algorithms whose performance can vary substantially with the characteristics of the data and model. In practice, this selection is frequently based on trial and error, requiring repeated model fitting and computational assessment. We propose a meta-learning framework that formulates Bayesian computational strategy selection as a data-dependent prediction problem. A simulation-based meta-database is constructed by generating survival datasets under systematically varied sample sizes, model dimensions, predictor correlations, and censoring proportions. Four Bayesian computational strategies—Standard Random-Walk MCMC, Adaptive MCMC, Hamiltonian Monte Carlo, and Reversible Jump MCMC—are evaluated across the simulated environments. Estimation accuracy, sampling efficiency, and computational cost are integrated through a multi-criteria utility function to identify the preferred computational strategy for each dataset. Machine-learning models are then trained to learn the relationship between survival-data characteristics and the resulting algorithm-selection decisions. The learned recommendation models are evaluated using three independent real survival datasets: the German Breast Cancer Study Group breast cancer, Veteran lung cancer, and primary biliary cirrhosis datasets. Importantly, the real-data evaluation is conducted without retraining or recalibration of the meta-learning models. The proposed framework provides a systematic approach for transferring computational experience from simulated survival environments to new problems. By treating Bayesian algorithm selection as a data-dependent decision rather than assuming a universally optimal MCMC strategy, the framework offers a practical approach for reducing trial-and-error computation and supporting evidence-based selection of Bayesian computational methods.
VL  - 15
IS  - 5
ER  -

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Author Information

Khalid Salah

College of Science & Technology, Al-Quds University, Abu Dis, Palestine

Contact Email

http://orcid.org/0000-0001-9893-8379
Afnan Salah

Department of General Medicine, Morriston Hospital, Swansea, United Kingdom

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APA Style

Salah, K., Salah, A. (2026). Meta-Learning for Bayesian Computation in Survival Analysis: A Machine Learning Framework for Intelligent Bayesian Algorithm Recommendation. American Journal of Theoretical and Applied Statistics, 15(5), 257-275. https://doi.org/10.11648/j.ajtas.20261505.16

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ACS Style

Salah, K.; Salah, A. Meta-Learning for Bayesian Computation in Survival Analysis: A Machine Learning Framework for Intelligent Bayesian Algorithm Recommendation. Am. J. Theor. Appl. Stat. 2026, 15(5), 257-275. doi: 10.11648/j.ajtas.20261505.16

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AMA Style

Salah K, Salah A. Meta-Learning for Bayesian Computation in Survival Analysis: A Machine Learning Framework for Intelligent Bayesian Algorithm Recommendation. Am J Theor Appl Stat. 2026;15(5):257-275. doi: 10.11648/j.ajtas.20261505.16

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@article{10.11648/j.ajtas.20261505.16,
  author = {Khalid Salah and Afnan Salah},
  title = {Meta-Learning for Bayesian Computation in Survival Analysis: A Machine Learning Framework for Intelligent Bayesian Algorithm Recommendation},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {15},
  number = {5},
  pages = {257-275},
  doi = {10.11648/j.ajtas.20261505.16},
  url = {https://doi.org/10.11648/j.ajtas.20261505.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20261505.16},
  abstract = {Bayesian computation for survival analysis often involves selecting among alternative Markov chain Monte Carlo (MCMC) algorithms whose performance can vary substantially with the characteristics of the data and model. In practice, this selection is frequently based on trial and error, requiring repeated model fitting and computational assessment. We propose a meta-learning framework that formulates Bayesian computational strategy selection as a data-dependent prediction problem. A simulation-based meta-database is constructed by generating survival datasets under systematically varied sample sizes, model dimensions, predictor correlations, and censoring proportions. Four Bayesian computational strategies—Standard Random-Walk MCMC, Adaptive MCMC, Hamiltonian Monte Carlo, and Reversible Jump MCMC—are evaluated across the simulated environments. Estimation accuracy, sampling efficiency, and computational cost are integrated through a multi-criteria utility function to identify the preferred computational strategy for each dataset. Machine-learning models are then trained to learn the relationship between survival-data characteristics and the resulting algorithm-selection decisions. The learned recommendation models are evaluated using three independent real survival datasets: the German Breast Cancer Study Group breast cancer, Veteran lung cancer, and primary biliary cirrhosis datasets. Importantly, the real-data evaluation is conducted without retraining or recalibration of the meta-learning models. The proposed framework provides a systematic approach for transferring computational experience from simulated survival environments to new problems. By treating Bayesian algorithm selection as a data-dependent decision rather than assuming a universally optimal MCMC strategy, the framework offers a practical approach for reducing trial-and-error computation and supporting evidence-based selection of Bayesian computational methods.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Meta-Learning for Bayesian Computation in Survival Analysis: A Machine Learning Framework for Intelligent Bayesian Algorithm Recommendation
AU  - Khalid Salah
AU  - Afnan Salah
Y1  - 2026/09/22
PY  - 2026
N1  - https://doi.org/10.11648/j.ajtas.20261505.16
DO  - 10.11648/j.ajtas.20261505.16
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 257
EP  - 275
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20261505.16
AB  - Bayesian computation for survival analysis often involves selecting among alternative Markov chain Monte Carlo (MCMC) algorithms whose performance can vary substantially with the characteristics of the data and model. In practice, this selection is frequently based on trial and error, requiring repeated model fitting and computational assessment. We propose a meta-learning framework that formulates Bayesian computational strategy selection as a data-dependent prediction problem. A simulation-based meta-database is constructed by generating survival datasets under systematically varied sample sizes, model dimensions, predictor correlations, and censoring proportions. Four Bayesian computational strategies—Standard Random-Walk MCMC, Adaptive MCMC, Hamiltonian Monte Carlo, and Reversible Jump MCMC—are evaluated across the simulated environments. Estimation accuracy, sampling efficiency, and computational cost are integrated through a multi-criteria utility function to identify the preferred computational strategy for each dataset. Machine-learning models are then trained to learn the relationship between survival-data characteristics and the resulting algorithm-selection decisions. The learned recommendation models are evaluated using three independent real survival datasets: the German Breast Cancer Study Group breast cancer, Veteran lung cancer, and primary biliary cirrhosis datasets. Importantly, the real-data evaluation is conducted without retraining or recalibration of the meta-learning models. The proposed framework provides a systematic approach for transferring computational experience from simulated survival environments to new problems. By treating Bayesian algorithm selection as a data-dependent decision rather than assuming a universally optimal MCMC strategy, the framework offers a practical approach for reducing trial-and-error computation and supporting evidence-based selection of Bayesian computational methods.
VL  - 15
IS  - 5
ER  -

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