Perfect simulation using dominating processes on ordered spaces, with application to locally stable point processes

By Wilfrid S. Kendall, Jesper Møller. Advances in Applied Probability, 2000

229 citations recorded by the source corpus.

Subfields: Applied Mathematics, Mathematical Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a97e48f06a037f0e3c98c78a59e281b28f2e6de8

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Citations in the corpus

Handbook of Spatial Statistics (2008, background): “1 Example: reseeding plants Armstrong (1991) considered the locations of 6378 plants from 67 species on a 22 m by 22 m observation window W in the south western area of Western Australia. The plants have adapted to regular natural fires, where resprouting species survive the fire, while seeding spec”
Markov Chains and Mixing Times (2008, methodology): “Before proceeding we mention that there are other algorithms that may be used for generating perfect samples from the stationary distribution of a Markov chain, including Fill’s algorithm (Fill, 1998, Fill, Machida, Murdoch, and Rosenthal, 2000), “dominated CFTP” (Kendall and Møller, 2000), “read-on”
An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants (2006, methodology): “Such point processes can be simulated perfectly by an extension of the Propp-Wilson CFTP algorithm, called dominated CFTP, see Kendall and Møller (2000).”
Markov Chains and Mixing Times: Second Edition (2017, methodology): “Before proceeding, we mention that there are other algorithms that may be used for generating perfect samples from the stationary distribution of a Markov chain, including Fill’s algorithm (Fill, 1998; Fill, Machida, Murdoch, and Rosenthal, 2000), “dominated CFTP” (Kendall and Møller, 2000), “readon”
Detecting filamentary pattern in the cosmic web: a catalogue of filaments for the SDSS (2013, methodology): “…to simulate marked point processes: spatial birth-and-death processes, Metropolis–Hastings (MH) algorithms, reversible jump dynamics or more recent exact simulation techniques (Geyer & Møller 1994; Green 1995; Geyer 1999; Kendall & Møller 2000; van Lieshout 2000; van Lieshout & Stoica 2006).”
Likelihood and Non‐parametric Bayesian MCMC Inference for Spatial Point Processes Based on Perfect Simulation and Path Sampling (2003, methodology): “Section 2 provides a short description of perfect simulation based on the dominated coupling from the past algorithm (dominated CFTP; Kendall, 1998; Kendall & Møller, 2000), and shows some empirical findings for the running time of the algorithm.”
Determinantal Point Processes (2016, background): “[19], [20], [23] and Algorithm 2 below).”
Generalised shot noise Cox processes (2005, background): “However, dominated coupling from the past can be used for making a perfect simulation of cent if this process is locally stable [21].”
Bayesian Analysis of Markov Point Processes (2006, methodology): “For γ 1, typical realisations look more regular than in the case γ = 1.”
Residual analysis for spatial point processes (with discussion) (2005, methodology): “It was obtained using a perfect sampler based on spatial birth-death processes as in [63, 64] using C code kindly supplied by Kasper K.”
General state space Markov chains and MCMC algorithms (2004, background): “Kendall and Møller [45], Thönnes [91], and Fill et al.”