Mathematical Sciences: An Interdisciplinary Meeting on Recent Developments in the Theory and Application of Markov Chain Monte Carlo Numerical Models

Zellner 9629834 研究者和同事们组织了一个关于马尔可夫链蒙特卡罗（MCMC）技术研讨会。 这些技术最初是在数学和物理学中发展起来的，现在已经在许多科学和应用领域中发挥了重要作用。 它们的使用使研究人员和应用工作者能够解决许多理论和应用问题。 虽然MCMC技术已经被证明具有很大的价值，但还需要做更多的工作来改进它们。 例如，正在开发新的方法来加速MCMC技术的收敛，检查MCMC输出的收敛性和准确性，并提供在用户友好的交互式环境中实现MCMC技术所需的计算机系统和程序。 这些是研讨会上讨论的主题的子集，包括新应用程序。 研究者和同事们组织了一个关于马尔可夫链蒙特卡罗（MCMC）技术研讨会。 蒙特卡罗技术是对数量进行概率估计的方法。 例如，复杂形状的体积可以通过在嵌入到更简单的形状中时对形状进行随机采样来估计。 MCMC技术已经在解决科学、工业和政府的重要问题中具有重要价值。 在这方面，模型被用来解释过去的行为，预测未来的结果和评估拟议的政策。 得出好的解决方案涉及所需的计算，在许多情况下，最好使用MCMC技术进行计算，例如与全球变暖分析，金融资产定价，行业人事决策，在线质量控制和政府政策问题有关。 更好的MCMC技术和更知情的用户，有助于更好地解决这些和其他私人和公共科学和政策问题。

Zellner 9629834 the investigator and colleagues organize a workshop on Markov chain Monte Carlo (MCMC) techniques. These techniques, originally developed in mathematics and physics, have come to be of great importance in many sciences and fields of application. Their use allows researchers and applied workers to solve many theoretical and applied problems. While MCMC techniques have already been shown to be of great value, more work has to be done to improve them. For example, new methods are being developed to speed convergence of MCMC techniques, to check on the convergence and accuracy of MCMC output, and to provide computer systems and programs needed to implement MCMC techniques in a user-friendly, interactive environment. These are a subset of the topics, including new applications, that are discussed at the workshop. the investigator and colleagues organize a workshop on Markov chain Monte Carlo (MCMC) techniques. Monte Carlo techniques are ways to make probabilistic estimates of quantities. For example, the volumes of a complicated shape can be estimated by making random samples of the shape when embedded in a simpler shape. MCMC techniques have already been of great value in solving important problems in science, industry and government. In this connection, models are employed to explain past behavior, predict future outcomes and evaluate proposed policies. Deriving good solutions involves needed calculations that are best carried out by use of MCMC techniques in many instances, e.g. in connection with the analysis of global warming, financial asset-pricing, industry personnel decisions, on-line quality control and governmental policy problems. Better MCMC techniques, and more informed users of them, contribute to better solutions to these and other private and public scientific and policy problems.