Perfect sampling for Gibbs point processes using partial rejection sampling
DOI10.3150/19-BEJ1184zbMath1436.82008arXiv1901.05624MaRDI QIDQ2174993
Publication date: 27 April 2020
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1901.05624
Strauss processpairwise interaction processperfect samplinghard-core processarea-interaction processpartial-rejection samplingpenetrable spheres mixture model
Exact distribution theory in statistics (62E15) Sampling theory, sample surveys (62D05) Interacting random processes; statistical mechanics type models; percolation theory (60K35) Sample path properties (60G17) Lattice systems (Ising, dimer, Potts, etc.) and systems on graphs arising in equilibrium statistical mechanics (82B20) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)
Uses Software
Cites Work
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- Spatial birth-death swap chains
- An interruptible algorithm for perfect sampling via Markov chains
- Area-interaction point processes
- Fast approximation of the intensity of Gibbs point processes
- Bounds on the artificial phase transition for perfect simulation of hard core Gibbs processes
- Perfect simulation for interacting point processes, loss networks and Ising models.
- Perfect simulation using dominating processes on ordered spaces, with application to locally stable point processes
- Uniform sampling through the Lovasz local lemma
- Rejection- and importance-sampling-based perfect simulation for Gibbs hard-sphere models
- On Local Distributed Sampling and Counting
- What Can be Sampled Locally?
- Bayesian Analysis of Markov Point Processes
- An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants
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