Synthesizing open worlds with constraints using locally annealed reversible jump MCMC

@article{Yeh2012SynthesizingOW,
  title={Synthesizing open worlds with constraints using locally annealed reversible jump MCMC},
  author={Yi-Ting Yeh and Lingfeng Yang and Matthew Watson and Noah D. Goodman and Pat Hanrahan},
  journal={ACM Trans. Graph.},
  year={2012},
  volume={31},
  pages={56:1-56:11}
}
We present a novel Markov chain Monte Carlo (MCMC) algorithm that generates samples from transdimensional distributions encoding complex constraints. We use factor graphs, a type of graphical model, to encode constraints as factors. Our proposed MCMC method, called locally annealed reversible jump MCMC, exploits knowledge of how dimension changes affect the structure of the factor graph. We employ a sequence of annealed distributions during the sampling process, allowing us to explore the state… CONTINUE READING
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