We propose a method to construct a proposal density for the Metropolis-Hastings algorithm in Markov Chain Monte Carlo (MCMC) simulations of the GARCH model. The proposal density is constructed adaptively by using the data sampled by the MCMC method itself. It turns out that autocorrelations between the data generated with our adaptive proposal density are greatly reduced. Thus it is concluded t...

The MCMC procedure, first released in SAS/STAT® 9.2, provides a flexible environment for fitting a wide range of Bayesian statistical models. Key enhancements in SAS/STAT 9.22 and 9.3 offer additional functionality and improved performance. The RANDOM statement provides a convenient way to specify linear and nonlinear random-effects models along with substantially improved performance. The 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...

To improve the accuracy of coal and gas prominence prediction, an improved sparrow search algorithm (ISSA) optimized support vector machine (SVM) based on Markov chain Monte Carlo (MCMC) filling prediction model were proposed. The mean value data after in missing values using MCMC was 2.282, with a standard deviation 0.193. Compared fill method (Mean), random forest (random forest, RF), K-neare...

INTRODUCTION Researchers and consultants have promoted expansion of Medi-Cal managed-care (MCMC) to additional Medi-Cal beneficiaries currently covered under the Medi-Cal Fee-forService (FFS) program to achieve greater cost efficiency and quality of care. Proponents have also promoted MCMC as a cost-effective way to expand state-subsidized health insurance for many of the State’s 6.5 million un...

Tasks such as record linkage and multi-target tracking, which involve reconstructing the set of objects that underlie some observed data, are particularly challenging for probabilistic inference. Recent work has achieved efficient and accurate inference on such problems using Markov chain Monte Carlo (MCMC) techniques with customized proposal distributions. Currently, implementing such a system...