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Efficient calculation of the normalizing constant of the autologistic and related models on the cylinder and lattice.

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Efficient calculation of the normalizing constant of the autologistic and related models on the cylinder and lattice. / Pettitt, Anthony; Friel, N.; Reeves, R.
In: Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 65, No. 1, 02.2003, p. 235-246.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Pettitt, A, Friel, N & Reeves, R 2003, 'Efficient calculation of the normalizing constant of the autologistic and related models on the cylinder and lattice.', Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 65, no. 1, pp. 235-246. https://doi.org/10.1111/1467-9868.00383

APA

Pettitt, A., Friel, N., & Reeves, R. (2003). Efficient calculation of the normalizing constant of the autologistic and related models on the cylinder and lattice. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 65(1), 235-246. https://doi.org/10.1111/1467-9868.00383

Vancouver

Pettitt A, Friel N, Reeves R. Efficient calculation of the normalizing constant of the autologistic and related models on the cylinder and lattice. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2003 Feb;65(1):235-246. doi: 10.1111/1467-9868.00383

Author

Pettitt, Anthony ; Friel, N. ; Reeves, R. / Efficient calculation of the normalizing constant of the autologistic and related models on the cylinder and lattice. In: Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2003 ; Vol. 65, No. 1. pp. 235-246.

Bibtex

@article{206f691edd6e4611b5258268f32aa1b2,
title = "Efficient calculation of the normalizing constant of the autologistic and related models on the cylinder and lattice.",
abstract = "Motivated by the autologistic model for the analysis of spatial binary data on the two-dimensional lattice, we develop efficient computational methods for calculating the normalizing constant for models for discrete data defined on the cylinder and lattice. Because the normalizing constant is generally unknown analytically, statisticians have developed various ad hoc methods to overcome this difficulty. Our aim is to provide computationally and statistically efficient methods for calculating the normalizing constant so that efficient likelihood-based statistical methods are then available for inference. We extend the so-called transition method to find a feasible computational method of obtaining the normalizing constant for the cylinder boundary condition. To extend the result to the free-boundary condition on the lattice we use an efficient path sampling Markov chain Monte Carlo scheme. The methods are generally applicable to association patterns other than spatial, such as clustered binary data, and to variables taking three or more values described by, for example, Potts models.",
author = "Anthony Pettitt and N. Friel and R. Reeves",
note = "RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research",
year = "2003",
month = feb,
doi = "10.1111/1467-9868.00383",
language = "English",
volume = "65",
pages = "235--246",
journal = "Journal of the Royal Statistical Society: Series B (Statistical Methodology)",
issn = "1369-7412",
publisher = "Wiley-Blackwell",
number = "1",

}

RIS

TY - JOUR

T1 - Efficient calculation of the normalizing constant of the autologistic and related models on the cylinder and lattice.

AU - Pettitt, Anthony

AU - Friel, N.

AU - Reeves, R.

N1 - RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research

PY - 2003/2

Y1 - 2003/2

N2 - Motivated by the autologistic model for the analysis of spatial binary data on the two-dimensional lattice, we develop efficient computational methods for calculating the normalizing constant for models for discrete data defined on the cylinder and lattice. Because the normalizing constant is generally unknown analytically, statisticians have developed various ad hoc methods to overcome this difficulty. Our aim is to provide computationally and statistically efficient methods for calculating the normalizing constant so that efficient likelihood-based statistical methods are then available for inference. We extend the so-called transition method to find a feasible computational method of obtaining the normalizing constant for the cylinder boundary condition. To extend the result to the free-boundary condition on the lattice we use an efficient path sampling Markov chain Monte Carlo scheme. The methods are generally applicable to association patterns other than spatial, such as clustered binary data, and to variables taking three or more values described by, for example, Potts models.

AB - Motivated by the autologistic model for the analysis of spatial binary data on the two-dimensional lattice, we develop efficient computational methods for calculating the normalizing constant for models for discrete data defined on the cylinder and lattice. Because the normalizing constant is generally unknown analytically, statisticians have developed various ad hoc methods to overcome this difficulty. Our aim is to provide computationally and statistically efficient methods for calculating the normalizing constant so that efficient likelihood-based statistical methods are then available for inference. We extend the so-called transition method to find a feasible computational method of obtaining the normalizing constant for the cylinder boundary condition. To extend the result to the free-boundary condition on the lattice we use an efficient path sampling Markov chain Monte Carlo scheme. The methods are generally applicable to association patterns other than spatial, such as clustered binary data, and to variables taking three or more values described by, for example, Potts models.

U2 - 10.1111/1467-9868.00383

DO - 10.1111/1467-9868.00383

M3 - Journal article

VL - 65

SP - 235

EP - 246

JO - Journal of the Royal Statistical Society: Series B (Statistical Methodology)

JF - Journal of the Royal Statistical Society: Series B (Statistical Methodology)

SN - 1369-7412

IS - 1

ER -