Issues in the Multiple Try Metropolis mixing
نویسندگان
چکیده
منابع مشابه
Adaptive Component-wise Multiple-Try Metropolis Sampling
One of the most widely used samplers in practice is the component-wise MetropolisHastings (CMH) sampler that updates in turn the components of a vector spaces Markov chain using accept-reject moves generated from a proposal distribution. When the target distribution of a Markov chain is irregularly shaped, a ‘good’ proposal distribution for one part of the state space might be a ‘poor’ one for ...
متن کاملOn Multiple Try Schemes and the Particle Metropolis-hastings Algorithm
Markov Chain Monte Carlo (MCMC) methods are well-known Monte Carlo methodologies, widely used in different fields for statistical inference and stochastic optimization. The Multiple Try Metropolis (MTM) algorithm is an extension of the standard Metropolis-Hastings (MH) algorithm in which the next state of the chain is chosen among a set of candidates, according to certain weights. The Particle ...
متن کاملCoherent Metropolis Light Transport with Multiple-Try Mutations
We present in this paper an effective way to implement coherent versions of Metropolis Light Transport (MLT) by using a class of Multiple-Try mutation strategies. Indeed, even if MLT is an unconditionally robust rendering technique which can handle any kind of lighting configurations, it does not exploit any computation coherency. For example, it is difficult to cluster similar light rays into ...
متن کاملA generalization of the Multiple-try Metropolis algorithm for Bayesian estimation and model selection
We propose a generalization of the Multipletry Metropolis (MTM) algorithm of Liu et al. (2000), which is based on drawing several proposals at each step and randomly choosing one of them on the basis of weights that may be arbitrary chosen. In particular, for Bayesian estimation we also introduce a method based on weights depending on a quadratic approximation of the posterior distribution. The...
متن کاملA generalized Multiple-try Metropolis version of the Reversible Jump algorithm
The Reversible Jump (RJ) algorithm (Green, 1995) is one of the most used Markov chain Monte Carlo algorithms for Bayesian estimation and model selection. We propose a generalized Multiple-try version of this algorithm which is based on drawing several proposals at each step and randomly choosing one of them on the basis of weights (selection probabilities) that may be arbitrary chosen. Along th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Statistics
سال: 2016
ISSN: 0943-4062,1613-9658
DOI: 10.1007/s00180-016-0643-9