The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
A tutorial on adaptive MCMC
Open accessOverview
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
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
A Stochastic Approximation Method
Robbins, H., & Monro, S. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729586 · Cited by 9,643
Weak convergence and optimal scaling of random walk Metropolis algorithms
Roberts, G. O., Gelman, A., & Gilks, W. R. (1997) · The Annals of Applied Probability · doi:10.1214/aoap/1034625254 · Cited by 1,693
Adaptive stochastic approximation by the simultaneous perturbation method
Spall, J. C. (2000) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2000.880982 · Cited by 424
Cited by
MCMC Using Hamiltonian Dynamics
Neal, R. M. (2011) · Handbook of Markov Chain Monte Carlo · doi:10.1201/b10905-6 · Cited by 2,574
Riemann manifold langevin and hamiltonian monte carlo methods
Girolami, M., & Calderhead, B. (2011) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2010.00765.x · Cited by 1,473
StarBEAST2 brings faster species tree inference and accurate estimates of substitution rates
Ogilvie, H. A., Bouckaert, R. R., & Drummond, A. J. (2017) · Molecular Biology and Evolution · doi:10.1093/molbev/msx126 · Cited by 469
Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy
Sharma, S. (2017) · Annual Review of Astronomy and Astrophysics · doi:10.1146/annurev-astro-082214-122339 · Cited by 298
Acerbi, L., Dokka, K., Angelaki, D. E., & Ma, W. J. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006110 · Cited by 111
Metadata
- Authors
- Andrieu, C., & Thoms, J.
- Venue
- Statistics and Computing
- Published
- 2008-12
- Volume
- 18
- Issue
- 4
- Pages
- 343-373
- ISSN
- 0960-3174 (ISSN-L)
- 1573-1375
- OpenAlex
- W2135973421
- Cited by
- 903
- References
- 4
- FWCI
- 27.01
- 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: A tutorial on adaptive MCMC. https://app.cochat.ai/papers/10.1007/s11222-008-9110-y
- The paper
- Andrieu, C., & Thoms, J. (2008). A tutorial on adaptive MCMC. Statistics and Computing. https://doi.org/10.1007/s11222-008-9110-y
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.
