Notes on “Coupling from the Past”

نویسنده

  • Michael Y. Hu
چکیده

The goal of a Markov Chain Monte Carlo (MCMC) simulation is to generate samples from a target probability distribution π by simulating a Markov chain whose stationary distribution is π. However, often this ideal is not achieved, and the practitioner actually samples from an approximate distribution π̃ that is close to π in variation distance. These circumstances have spawned an array of literature addressing Markov chain simulation convergence, including literature on an MCMC strategy called Coupling from the Past (CFTP) that can circumvent the convergence problem altogether to draw samples exactly from the target distribution.

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تاریخ انتشار 2014