Reversible jump Markov chain Monte Carlo computation and Bayesian model determination Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has... GREEN PETER J. - 《Biometrika》 被引量: 9227发表: 1995...
Markov chain Monte Carlotestlet modelparameter recoveryFor testlet response data, traditional item response theory (IRT) models are often not appropriate due to local dependence presented among items within a common testlet. Several testlet-based IRT models have been developed to model examinees' ...
Giudici, P., & Castelo, R. (2003). Improving Markov chain Monte Carlo model search for data mining.Machine Learning,50, 127–158. ArticleMATHGoogle Scholar Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications.Biometrika,57, 97–109. ...
The Markov Chain Monte Carlo sampling (MCMC) analyses, with four chains starting from random tree topology, were run between 10,000,000 generations for each combined dataset. Trees were sampled every 100 generations using a relative burn-in discarding the first 25% of sampled trees40. ...
However, both lack capabilities for flexible and efficient Markov chain Monte Carlo (MCMC) integration. Recently, the no-U-turn sampler (NUTS) MCMC algorithm has gained popularity for Bayesian inference through the software Stan because it is efficient for high dimensional, complex hierarchical models...
Markov Chain Introduction in R Monte Carlo Analysis in R Stock Market Predictions Next Week Capture errors, warnings and messages {golem} 0.3.2 is now available Convert column to categorical in R Which data science skills are important ($50,000 increase in salary in 6-months) A pr...
This package simplifies the calculation of odds ratios in binomial models. For GAMs, it also provides you with the power to insert your results into the smooth functions of your predictors! But let’s start with some basics… –This post refers to package version 0.3.0 – ...
摘要: CiteSeerX - Scientific documents that cite the following paper: Introducing Markov chain Monte Carlo. [8 关键词: Ordered by external client DOI: 10.1007/978-1-4899-4485-6_1 被引量: 970 年份: 1996 收藏 引用 批量引用 报错 分享 全部来源 免费下载 求助全文 ResearchGate ResearchGate (全网...
The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis {Hastings and Gibbs algorithms, and adaptive algorithms. All chapters include exercises and all R programs are available ...
Further, the Bayesian method and Markov Chain Monte Carlo (MCMC) simulation were used to obtain the updating of failure probability by introducing rehabilitation and in-line inspection (ILI) data. Failure probability was performed to study this difference using Monte Carlo simulation (MCS) to ...