Results 251 to 260 of about 132,359 (288)

The ADT evaluation method based on MCMC

2011 IEEE MTT-S International Microwave Workshop Series on Innovative Wireless Power Transmission: Technologies, Systems, and Applications, 2011
This paper proposes an accelerated degradation testing (ADT) evaluation method based on Markov Chain Monte Carlo (MCMC) method. Firstly the degradation model, reliability model and accelerated model of ADT are introduced; secondly, with the information above, the ADT evaluation method based on MCMC is proposed; Thirdly, the evaluation results of this ...
Lizhi Wang, Xiaoyang Li, Tongmin Jiang, Xiaotian Zhuang   +3 more
openaire   +1 more source

Interpret Model Complexity: Trans-Dimensional MCMC Method

2020
In the previous chapters, we witness the power of statistical inverse methods that used to sample from the posterior distribution of earth model parameters given the observed azimuthal resistivity measurements. The statistical inversion resolves the local minimum problem in the deterministic methods and tells the uncertainty of model parameters via the
Qiuyang Shen, Jiefu Chen, Xuqing Wu, Yueqin Huang, Zhu Han   +4 more
openaire   +1 more source

Estimating heterogeneous transmission with multiple infectives using MCMC methods

Statistics in Medicine, 2003
AbstractWe developed a general procedure for estimating the transmission probability adjusting for covariates when susceptibles are exposed to several infectives concurrently and taking correlation within transmission units into account. The procedure is motivated by a study estimating efficacy of pertussis vaccination based on the secondary attack ...
Haitao, Chu, Marie-Pierre, Préziosi, M Elizabeth, Halloran   +2 more
openaire   +2 more sources

Introduction to Simulation and MCMC Methods

2011
The purpose of this article is to provide an overview of Monte Carlo methods for generating variates from a target probability distribution that are based on Markov chains. These methods, called Markov chain Monte Carlo (MCMC) methods, are widely used to summarize complicated posterior distributions in Bayesian statistics and econometrics. This article
openaire   +1 more source

Marginal Likelihood Calculation with MCMC Methods

2013
Markov Chain Monte Carlo (MCMC) methods have revolutionised Bayesian data analysis over the years by making the direct computation of posterior probability densities feasible on modern workstations. However, the calculation of the prior predictive, the marginal likelihood, has proved to be notoriously difficult with standard techniques. In this chapter
openaire   +1 more source

MCMC Methods for Continuous-Time Financial Econometrics

SSRN Electronic Journal, 2003
Publisher Summary This chapter describes various Markov Chain Monte Carlo (MCMC) methods for exploring the posterior distributions generated by continuous-time asset pricing models. The MCMC methods are particularly well suited for continuous-time finance applications for several reasons.
Michael S. Johannes, Nick Polson
openaire   +1 more source

MCMC methods for stochastic epidemic models

2003
Abstract The previous article highlighted some of the challenges involved in performing statistical inference using stochastic models for infectious diseases. These challenges arise due to both the inherent features of infectious disease outbreak data, for instance a high level of dependency in such data and a lack of complete ...
openaire   +1 more source

MCMC Methods for Periodic AR-Arch Models

IFAC Proceedings Volumes, 2001
Abstract Many economic time series reveal periodic and seasonal patterns which can be modeled by periodic AR processes. Using a conjugate normal-gamma model we suggest to model seasonal data by a hierarchical prior distribution. We extend this approach to include periodic ARCH models which we call a PAR-GARCH model. A Metropolis-Hastings step is used
openaire   +1 more source

An Approximate MCMC Method for Convex Hulls

2019
Markov chain Monte Carlo (MCMC) is an extremely popular class of algorithms for computing summaries of posterior distributions. One problem for MCMC in the so-called Big Data regime is the growing computational cost of most MCMC algorithms. Most popular and basic MCMC algorithms, like Metropolis-Hastings algorithm (MH) and Gibbs algorithm, have to take
openaire   +2 more sources

MCMC methods based modulation classification

IET International Conference on Wireless Mobile and Multimedia Networks Proceedings (ICWMMN 2006), 2006
null Bao Dan, null Yang Shao-quan
openaire   +1 more source