The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Bayes in the sky: Bayesian inference and model selection in cosmology
Open accessAbstract
The application of Bayesian methods in cosmology and astrophysics has flourished over the past decade, spurred by data sets of increasing size and complexity. In many respects, Bayesian methods have proven to be vastly superior to more traditional statistical tools, offering the advantage of higher efficiency and of a consistent conceptual basis for dealing with the problem of induction in the presence of uncertainty. This trend is likely to continue in the future, when the way we collect, manipulate and analyse observations and compare them with theoretical models will assume an even more central role in cosmology. This review is an introduction to Bayesian methods in cosmology and astrophysics and recent results in the field. I first present Bayesian probability theory and its conceptual underpinnings, Bayes' Theorem and the role of priors. I discuss the problem of parameter inference and its general solution, along with numerical techniques such as Monte Carlo Markov Chain methods. I then review the theory and application of Bayesian model comparison, discussing the notions of Bayesian evidence and effective model complexity, and how to compute and interpret those quantities. Recent developments in cosmological parameter extraction and Bayesian cosmological model building are summarised, highlighting the challenges that lie ahead.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Riess, A. G., Strolger, L., Tonry, J., Casertano, S., Ferguson, H. C., Mobasher, B., Challis, P., Filippenko, A. V., Jha, S., Li, W., et al. (2004) · The Astrophysical Journal · doi:10.1086/383612 · Cited by 4,315
Bernardo, J. M., & Smith, A. F. M. (1994) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316870 · Cited by 2,178
Testing a Point Null Hypothesis: The Irreconcilability of P Values and Evidence
Berger, J. O., & Sellke, T. (1987) · Journal of the American Statistical Association · doi:10.2307/2289131 · Cited by 548
Cited by
Planck 2018 results: X. Constraints on inflation
Planck Collaboration, Akrami, Y., Arroja, F., Ashdown, M., Aumont, J., Baccigalupi, C., Ballardini, M., Banday, A. J., Barreiro, R. B., Bartolo, N., et al. (2020) · Astronomy & Astrophysics · doi:10.1051/0004-6361/201833887 · Cited by 2,442
Cosmology and Fundamental Physics with the Euclid Satellite
The Euclid Theory Working Group, Amendola, L., Appleby, S., Bacon, D., Baker, T., Baldi, M., Bartolo, N., Blanchard, A., Bonvin, C., Borgani, S., et al. (2013) · Living Reviews in Relativity · doi:10.12942/lrr-2013-6 · Cited by 899
Self-citationDESI DR2 results. II. Measurements of baryon acoustic oscillations and cosmological constraints
Abdul Karim, M., Aguilar, J., Ahlen, S., Alam, S., Allen, L., Prieto, C. A., Alves, O., Anand, A., Andrade, U., Armengaud, E., et al. (2025) · Physical Review D · doi:10.1103/tr6y-kpc6 · Cited by 856
Planck evidence for a closed Universe and a possible crisis for cosmology
Di Valentino, E., Melchiorri, A., & Silk, J. (2019) · Nature Astronomy · doi:10.1038/s41550-019-0906-9 · Cited by 673
Event Horizon Telescope Collaboration, Akiyama, K., Alberdi, A., Alef, W., Algaba, J. C., Anantua, R., Asada, K., Azulay, R., Bach, U., Baczko, A.-K., et al. (2022) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/ac6736 · Cited by 369
“The value of serves as a model comparison metric that naturally balances improvements in fit quality against increases in model complexity, with larger values of indicating preferred models (see, e.g., Trotta 2008).”
Background
Metadata
- Authors
- Trotta, R.
- Venue
- Contemporary Physics
- Publisher
- Informa UK Limited
- Published
- 2008-03
- Volume
- 49
- Issue
- 2
- Pages
- 71-104
- ISSN
- 0010-7514 (ISSN-L)
- 1366-5812
- OpenAlex
- W2021748112
- Cited by
- 1,253
- References
- 4
- FWCI
- 18.27
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Bayes in the sky: Bayesian inference and model selection in cosmology. https://app.cochat.ai/papers/10.1080/00107510802066753
- The paper
- Trotta, R. (2008). Bayes in the sky: Bayesian inference and model selection in cosmology. Contemporary Physics. https://doi.org/10.1080/00107510802066753
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
