Non-homogeneous Poisson process intensity modeling and estimation using measure transport-Reference-Cited by-同舟云学术

Non-homogeneous Poisson process intensity modeling and estimation using measure transport

Published:2023-02-01 Issue:1 Volume:29 Page:
ISSN:1350-7265
Container-title:Bernoulli
language:
Short-container-title:Bernoulli

Author:

Ng Tin Lok James¹,Zammit-Mangion Andrew²

Affiliation:

1. School of Computer Science and Statistics, Trinity College Dublin, Ireland

2. School of Mathematics and Applied Statistics, University of Wollongong, Australia

Publisher

Bernoulli Society for Mathematical Statistics and Probability

Subject

Statistics and Probability

Reference42 articles.

1. Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis. Monographs on Statistics and Applied Probability. London: CRC Press. 10.1007/978-1-4899-3324-9

2. Marzouk, Y., Moselhy, T., Parno, M. and Spantini, A. (2017). Sampling via measure transport: An introduction. In Handbook of Uncertainty Quantification. Vol. 1, 2, 3 785–825. Cham: Springer.

3. Brenier, Y. (1991). Polar factorization and monotone rearrangement of vector-valued functions. Comm. Pure Appl. Math. 44 375–417. 10.1002/cpa.3160440402

4. Adams, R.P., Murray, I. and MacKay, D.J. C. (2009). Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities. In Proceedings of the 26th Annual International Conference on Machine Learning 9–16.

5. Bachl, F.E., Lindgren, F., Borchers, D.L. and Illian, J.B. (2019). Inlabru: An R package for Bayesian spatial modelling from ecological survey data. Methods Ecol. Evol. 10 760–766.

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