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Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models

Johnson, Griffiths and Goldwater · 2007 · Advances in Neural Information Processing Systems 19

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Methods survey: bioinformatics and signal processing	SRC0D8NJBW275GE
Methods survey: emerging mathematics and machine learning	SRC0D8NJCBP11SK
Methods survey: linguistics and datasets	SRC0D8NJBM8396W
Survey: inductive methods from fourteen fields	SRC0D8NJDAY13ME

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Johnson, M., Griffiths, T., Goldwater, S. 2007. Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models. Advances in Neural Information Processing Systems 19.

@article{johnson2007adaptor,
  author = {Johnson, Mark and Griffiths, Thomas L. and Goldwater, Sharon},
  title = {Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models},
  journal = {Advances in Neural Information Processing Systems 19},
  year = {2007},
  doi = {10.7551/mitpress/7503.003.0085},
  url = {https://doi.org/10.7551/mitpress/7503.003.0085}
}

DOI: 10.7551/mitpress/7503.003.0085
Web: https://doi.org/10.7551/mitpress/7503.003.0085
Licence: unknown
Cite key: johnson2007adaptor

In the research Session 6: Phase 2: registered tests, brute force, five surveys; book v1.1ConsultedSession 9: The inquiry framework, the inductive battery, generated hypotheses and readingsConsulted

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v1 · 25 Oct 2026 · Voynich Manuscript: sync from the research ledger