Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 91

The DOI matches its Crossref record.

Fast simulation of truncated Gaussian distributions

Chopin, Nicolas·2011·Statistics and Computing
DOI 10.1007/s11222-009-9168-1
Open this paper in CoChat — free account

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s11222-009-9168-1

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.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

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

No references could be resolved yet.

Research works printed but not linked: 33

  1. Abramowitz, M., Stegun, I.: Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Table. Dover, New York (1965)
  2. Ahrens, J.: A one-table method for sampling from continuous and discrete distributions. Computing 54(2), 127–146 (1995) · doi:10.1007/bf02238128
  3. Albert, J.H., Chib, S.: Bayesian analysis of binary and polychotomous response data. J. Am. Stat. Assoc. 88(422), 669–79 (1993) · DOI
  4. Blair, J., Edwards, C., Johnson, J.: Rational Chebyshev approximations for the inverse of the error function. Math. Comput. (1976) · doi:10.2307/2005402
  5. Chen, M., Deely, J.: Bayesian analysis for a constrained linear multiple regression problem for predicting the new crop of apples. J. Agric. Biol. Environ. Stat. 1(4), 467–489 (1996) · doi:10.2307/1400440
  6. Chib, S.: Bayes inference in the Tobit censored regression model. J. Econom. 51(1–2), 79–99 (1992) · DOI · Paper page
  7. Chib, S., Greenberg, E.: Analysis of multivariate probit models. Biometrika 85(2), 347 (1998) · DOI · Paper page
  8. Devroye, L.: Non-Uniform Random Variate Generation. Springer, New York (1986) · DOI · Paper page
  9. Evans, M., Swartz, T.: Random variable generation using concavity properties of transformed densities. J. Comput. Graph. Stat. 7(4), 514–528 (1998) · doi:10.2307/1390680
  10. Fernández, P., Ferrari, P., Grynberg, S.: Perfectly random sampling of truncated multinormal distributions. Adv. Appl. Probab. 39(4), 973–990 (2007) · doi:10.1239/aap/1198177235
  11. Gelfand, A., Smith, A., Lee, T.: Bayesian analysis of constrained parameter and truncated data problems using Gibbs sampling. J. Am. Stat. Assoc. 87(418), 523–532 (1992) · doi:10.2307/2290286
  12. Geweke, J.: Efficient simulation from the multivariate normal and Student-t distributions subject to linear constraints and the evaluation of constraint probabilities. Comput. Sci. Stat. 23, 571–578 (1991)
  13. Gilks, W., Wild, P.: Adaptive rejection sampling for Gibbs sampling. Appl. Stat. 41(2), 337–348 (1992) · DOI · Paper page
  14. Glasserman, P.: Monte Carlo Methods in Financial Engineering. Springer, Berlin (2004)
  15. Gulam Razul, S., Fitzgerald, W., Andrieu, C.: Bayesian model selection and parameter estimation of nuclear emission spectra using RJMCMC. Nucl. Inst. Methods Phys. Res. 497(2–3), 492–510 (2003) · doi:10.1016/s0168-9002(02)01807-7
  16. Hörmann, W.: A rejection technique for sampling from T-concave distributions. ACM Trans. Math. Softw. 21(2), 182–193 (1995) · doi:10.1145/203082.203089
  17. Hörmann, W.: Algorithm 802: an automatic generator for bivariate log-concave distributions. ACM Trans. Math. Softw. 26(1), 201–219 (2000) · doi:10.1145/347837.347908
  18. Hörmann, W., Leydold, J., Derflinger, G.: Automatic Nonuniform Random Variate Generation. Springer, Berlin (2004) · DOI
  19. Johnson, N., Kotz, S., Balakrishnan, N.: Continuous Univariate Distributions. Wiley, New York (1994)
  20. Leydold, J.: Automatic sampling with the ratio-of-uniforms method. ACM Trans. Math. Softw. 26(1), 78–98 (2000) · doi:10.1145/347837.347863
  21. Linardakis, M., Dellaportas, P.: Assessment of Athens’s metro passenger behaviour via a multiranked Probit model. J. R. Stat. Soc. C 52(2), 185–200 (2003) · DOI
  22. Marsaglia, G.: Generating a variable from the tail of the normal distribution. Technometrics 6(1), 101–102 (1964) · doi:10.2307/1266749
  23. Marsaglia, G., Tsang, W.W.: A fast, easily implemented method for sampling from decreasing or symmetric unimodal density functions. SIAM J. Sci. Stat. Comput. 5, 349–359 (1984) · doi:10.1137/0905026
  24. Marsaglia, G., Tsang, W.: The Ziggurat method for generating random variables. J. Stat. Soft. 5(8) (2000) · DOI · Paper page
  25. McCulloch, R., Rossi, P.: An exact likelihood analysis of the multinomial probit model. J. Econom. 64(1), 207–240 (1994) · DOI · Paper page
  26. Nobile, A.: A hybrid Markov chain for the Bayesian analysis of the multinomial probit model. Stat. Comput. 8(3), 229–242 (1998) · DOI
  27. Philippe, A., Robert, C.: Perfect simulation of positive Gaussian distributions. Stat. Comput. 13(2), 179–186 (2003) · DOI · Paper page
  28. Pitt, M., Chan, D., Kohn, R.: Efficient Bayesian inference for Gaussian copula regression models. Biometrika 93, 537–554 (2006) · DOI · Paper page
  29. Prekopa, A.: On logarithmic concave measures and functions. Acta Sci. Math. (Szeged) 34, 335–343 (1973)
  30. Robert, C.P.: Simulation of truncated normal variables. Stat. Comput. 5, 121–125 (1995) · DOI · Paper page
  31. Rue, H., Held, L.: Gaussian Markov Random Fields: Theory and Applications. Chapman & Hall/CRC, London (2005) · DOI · Paper page
  32. Tanner, M., Wong, W.: The calculation of posterior distributions by data augmentation. J. Am. Stat. Assoc. 82(398), 528–540 (1987) · DOI
  33. Wichura, M.: Algorithm AS 241: The percentage points of the normal distribution. Appl. Stat., 477–484 (1988) · doi:10.2307/2347330

Cited by

See all 91

Metadata

Authors
Chopin, N.
Venue
Statistics and Computing
Publisher
Springer Science and Business Media LLC
Published
2010-01-07
Volume
21
Issue
2
Pages
275-288
ISSN
  • 0960-3174
  • 1573-1375
DOI
10.1007/s11222-009-9168-1
Cited by
91
References
0
Language
en
Licence
www.springer.com/tdm (tdm)

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: Fast simulation of truncated Gaussian distributions. https://app.cochat.ai/papers/10.1007/s11222-009-9168-1
The paper
Chopin, N. (2011). Fast simulation of truncated Gaussian distributions. Statistics and Computing. https://doi.org/10.1007/s11222-009-9168-1

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.