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Cited by 394

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Automatic Bayesian Curve Fitting

Open access
Denison, D. G. T., Mallick, B. K., Smith, A. F. M.·1998·Journal of the Royal Statistical Society Series B: Statistical Methodology

Abstract

Summary: A method of estimating a variety of curves by a sequence of piecewise polynomials is proposed, motivated by a Bayesian model and an appropriate summary of the resulting posterior distribution. A joint distribution is set up over both the number and the position of the knots defining the piecewise polynomials. Throughout we use reversible jump Markov chain Monte Carlo methods to compute the posteriors. The methodology has been successful in giving good estimates for ‘smooth’ functions (i.e. continuous and differentiable) as well as functions which are not differentiable, and perhaps not even continuous, at a finite number of points. The methodology is extended to deal with generalized additive models.

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Metadata

Authors
Denison, D. G. T., Mallick, B. K., & Smith, A. F. M.
Venue
Journal of the Royal Statistical Society Series B: Statistical Methodology
Published
1998-07-01
Volume
60
Issue
2
Pages
333-350
ISSN
  • 1369-7412 (ISSN-L)
  • 1467-9868
OpenAlex
W2128860595
Cited by
394
References
1
FWCI
23.22
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Automatic Bayesian Curve Fitting. https://app.cochat.ai/papers/10.1111/1467-9868.00128
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Denison, D. G. T., Mallick, B. K., & Smith, A. F. M. (1998). Automatic Bayesian Curve Fitting. Journal of the Royal Statistical Society Series B: Statistical Methodology. https://doi.org/10.1111/1467-9868.00128

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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.