Orthogonal parallel MCMC methods for sampling and optimization

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Orthogonal parallel MCMC methods for sampling and optimization

Monte Carlo (MC) methods are widely used in statistics, signal processing and machinelearning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC)algorithms. In order to foster better exploration of the state space, specially in high-dimensional applications, several schemes employing multiple parallel MCMC chains have beenrecently introduced. In this work, ...

متن کامل

Generalizing Elliptical Slice Sampling for Parallel MCMC

Probabilistic models are conceptually powerful tools for finding structure in data, but their practical effectiveness is often limited by our ability to perform inference in them. Exact inference is frequently intractable, so approximate inference is often performed using Markov chain Monte Carlo (MCMC). To achieve the best possible results from MCMC, we want to efficiently simulate many steps ...

متن کامل

MCMC and Naive Parallel Gibbs Sampling

In this scribe, we are going to review the Parallel Monte Carlo Markov Chain (MCMC) method. First, we will recap of MCMC methods, particularly the Metropolis-Hasting and Gibbs Sampling algorithms. Then we will show the drawbacks of these classical MCMC methods as well as the Naive Parallel Gibbs Sampling approach. Finally, we will come up with the Sequential Monte Carlo and Parallel Inference f...

متن کامل

MCMC Methods for Sampling Function Space

Applied mathematics is concerned with developing models with predictive capability, and with probing those models to obtain qualitative and quantitative insight into the phenomena being modelled. Statistics is data-driven and is aimed at the development of methodologies to optimize the information derived from data. The increasing complexity of phenomena that scientists and engineers wish to mo...

متن کامل

Parallel MCMC with generalized elliptical slice sampling

Probabilistic models are conceptually powerful tools for finding structure in data, but their practical effectiveness is often limited by our ability to perform inference in them. Exact inference is frequently intractable, so approximate inference is often performed using Markov chain Monte Carlo (MCMC). To achieve the best possible results from MCMC, we want to efficiently simulate many steps ...

متن کامل

ذخیره 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: Digital Signal Processing

سال: 2016

ISSN: 1051-2004

DOI: 10.1016/j.dsp.2016.07.013