Adaptive Gibbs samplers
نویسندگان
چکیده
We consider various versions of adaptive Gibbs and Metropoliswithin-Gibbs samplers, which update their selection probabilities (and perhaps also their proposal distributions) on the fly during a run, by learning as they go in an attempt to optimise the algorithm. We present a cautionary example of how even a simple-seeming adaptive Gibbs sampler may fail to converge. We then present various positive results guaranteeing convergence of adaptive Gibbs samplers under certain conditions. AMS 2000 subject classifications: Primary 60J05, 65C05; secondary 62F15.
منابع مشابه
Adaptive Gibbs samplers and related MCMC methods
We consider various versions of adaptive Gibbs and Metropoliswithin-Gibbs samplers, which update their selection probabilities (and perhaps also their proposal distributions) on the fly during a run, by learning as they go in an attempt to optimise the algorithm. We present a cautionary example of how even a simple-seeming adaptive Gibbs sampler may fail to converge. We then present various pos...
متن کاملAdaptive MC and Gibbs Algorithms for Bayesian Model Averaging in Linear Regression Models
The MC3 (Madigan and York, 1995) and Gibbs (George and McCulloch, 1997) samplers are the most widely implemented algorithms for Bayesian Model Averaging (BMA) in linear regression models. These samplers draw a variable at random in each iteration using uniform selection probabilities and then propose to update that variable. This may be computationally inefficient if the number of variables is ...
متن کاملImplementing Random Scan Gibbs Samplers I
The Gibbs sampler, being a popular routine amongst Markov chain Monte Carlo sampling methodologies, has revolutionized the application of Monte Carlo methods in statistical computing practice. The performance of the Gibbs sampler relies heavily on the choice of sweep strategy, that is, the means by which the components or blocks of the random vector X of interest are visited and updated. We dev...
متن کاملSurprising Convergence Properties of Some Simple Gibbs Samplers Under Various Scans
We examine the convergence properties of some simple Gibbs sampler examples under various scans. We find some surprising results, including Gibbs samplers where deterministic-scan is much more efficient than random-scan, and other samplers where the opposite is true. We also present an example where the convergence takes precisely the same time with any fixed deterministic scan, but modifying t...
متن کاملStatic-parameter estimation in piecewise deterministic processes using particle Gibbs samplers
We develop particle Gibbs samplers for static-parameter estimation in discretelyobserved piecewise deterministic processes (pdps). pdps are stochastic processes that jump randomly at a countable number of stopping times but otherwise evolve deterministically in continuous time. A sequential Monte Carlo (smc) sampler for ltering in pdps has recently been proposed. We rst provide new insight into...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2010