منابع مشابه
Perfect Sampling Using Bounding Chains
Bounding chains are a technique that offers three benefits to Markov chain practitioners: a theoretical bound on the mixing time of the chain under restricted conditions, experimental bounds on the mixing time of the chain that are provably accurate and construction of perfect sampling algorithms when used in conjunction with protocols such as coupling from the past. Perfect sampling algorithms...
متن کاملAn Interruptible Algorithm for Perfect Sampling via Markov Chains Short Title: Perfect Sampling via Markov Chains
For a large class of examples arising in statistical physics known as attractive spin systems (e.g., the Ising model), one seeks to sample from a probability distribution π on an enormously large state space, but elementary sampling is ruled out by the infeasibility of calculating an appropriate normalizing constant. The same difficulty arises in computer science problems where one seeks to sam...
متن کاملPerfect Sampling of Harris Recurrent Markov Chains
We develop an algorithm for simulating \perfect" random samples from the invariant measure of a Harris recurrent Markov chain. The method uses backward coupling of embedded regeneration times, and works most eeectively for nite chains and for stochas-tically monotone chains even on continuous spaces, where paths may be sandwiched below \upper" and \lower" processes. Examples show that more naiv...
متن کاملPerfect Sampling of Markov Chains with Piecewise Homogeneous Events
Perfect sampling is a technique that uses coupling arguments to provide a sample from the stationary distribution of a Markov chain in a finite time without ever computing the distribution. This technique is very efficient if all the events in the system have monotonicity property. However, in the general (non-monotone) case, this technique needs to consider the whole state space, which limits ...
متن کاملExtension of Fill's perfect rejection sampling algorithm to general chains
We provide an extension of the perfect sampling algorithm of Fill (1998) to general chains, and describe how use of bounding processes can ease computational burden. Along the way, we unearth a simple connection between the Coupling From The Past (CFTP) algorithm originated by Propp and Wilson (1996) and our extension of Fill’s algorithm.
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ژورنال
عنوان ژورنال: The Annals of Applied Probability
سال: 2004
ISSN: 1050-5164
DOI: 10.1214/105051604000000080