MCMC GGUM

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

MCMC Learning

The theory of learning under the uniform distribution is rich and deep, with connections to cryptography, computational complexity, and the analysis of boolean functions to name a few areas. This theory however is very limited due to the fact that the uniform distribution and the corresponding Fourier basis are rarely encountered as a statistical model. A family of distributions that vastly gen...

متن کامل

Variational MCMC

We propose a new class of learning algorithms that combines variational approximation and Markov chain Monte Carlo (MCMC) simu­ lation. Naive algorithms that use the vari­ ational approximation as proposal distribu­ tion can perform poorly because this approx­ imation tends to underestimate the true vari­ ance and other features of the data. We solve this problem by introducing more so­ phistic...

متن کامل

CPSG-MCMC: Clustering-Based Preprocessing method for Stochastic Gradient MCMC

In recent years, stochastic gradient Markov Chain Monte Carlo (SG-MCMC) methods have been raised to process large-scale dataset by iterative learning from small minibatches. However, the high variance caused by naive subsampling usually slows down the convergence to the desired posterior distribution. In this paper, we propose an effective subsampling strategy to reduce the variance based on a ...

متن کامل

Type-Based MCMC

Most existing algorithms for learning latentvariable models—such as EM and existing Gibbs samplers—are token-based, meaning that they update the variables associated with one sentence at a time. The incremental nature of these methods makes them susceptible to local optima/slow mixing. In this paper, we introduce a type-based sampler, which updates a block of variables, identified by a type, wh...

متن کامل

Mcmc Using Graphical Models

Markov chain Monte Carlo techniques have revolutionized the field of Bayesian statistics. Their enormous power and their generalizability have rendered them the method of choice for statistical inference in many scientific disciplines. Their power is so great that they can even accommodate situations in which the structure of the statistical model itself is uncertain. However, the analysis of s...

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: Applied Psychological Measurement

سال: 2014

ISSN: 0146-6216,1552-3497

DOI: 10.1177/0146621614540514