Comparison of asymptotic variances of inhomogeneous Markov chains with application to Markov chain Monte Carlo methods
نویسندگان
چکیده
منابع مشابه
Putting Markov Chains Back into Markov Chain Monte Carlo
Markov chain theory plays an important role in statistical inference both in the formulation of models for data and in the construction of efficient algorithms for inference. The use of Markov chains in modeling data has a long history, however the use of Markov chain theory in developing algorithms for statistical inference has only become popular recently. Using mark-recapture models as an il...
متن کاملComparison of two Markov chain Monte Carlo (MCMC) methods
As the world advances, statisticians/mathematicians are being involved into more and more complex surveys for the development of society and human beings. Consequently, these complex survey data requires complicated and high-dimensional models for final analysis. We need sophisticated and efficient statistical/mathematical tools for estimation and prediction of these models. Frequently, we simu...
متن کاملSequentially Interacting Markov Chain Monte Carlo Methods
We introduce a novel methodology for sampling from a sequence of probability distributions of increasing dimension and estimating their normalizing constants. These problems are usually addressed using Sequential Monte Carlo (SMC) methods. The alternative Sequentially Interacting Markov Chain Monte Carlo (SIMCMC) scheme proposed here works by generating interacting non-Markovian sequences which...
متن کاملMarkov chain Monte Carlo methods in biostatistics.
Appropriate models in biostatistics are often quite complicated. Such models are typically most easily fit using Bayesian methods, which can often be implemented using simulation techniques. Markov chain Monte Carlo (MCMC) methods are an important set of tools for such simulations. We give an overview and references of this rapidly emerging technology along with a relatively simple example. MCM...
متن کاملParticle Markov chain Monte Carlo methods
Markov chain Monte Carlo and sequential Monte Carlo methods have emerged as the two main tools to sample from high dimensional probability distributions.Although asymptotic convergence of Markov chain Monte Carlo algorithms is ensured under weak assumptions, the performance of these algorithms is unreliable when the proposal distributions that are used to explore the space are poorly chosen and...
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: The Annals of Statistics
سال: 2014
ISSN: 0090-5364
DOI: 10.1214/14-aos1209