文件名称:MachineLearning
- 所属分类：
- 人工智能/神经网络/遗传算法
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- [PDF]
- 上传时间：
- 2012-11-26
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- 2.22mb
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- 曹*
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MCMC方法是一种重要的模拟计算方法，马尔可夫链蒙特卡尔理论(Markov chain Monte Carlo：MCMC)的研究对建立可实际应用的统计模型开辟了广阔的前景。90年代以来，很多应用问题都存在着分析对象比较复杂与正确识别模型结构的困难。现在根据MCMC理论，通过使用专用统计软件进行MCMC模拟，可解决许多复杂性问题。此外，得益于MCMC理论的运用，使得贝叶斯(Bayes)统计得到了再度复兴，以往被认为不可能实施计算的统计方法变得是很轻而易举了-MCMC method is an important simulation methods, Markov chain Mengtekaer theory (Markov chain Monte Carlo: MCMC) research on the establishment of the practical application of the statistical model can be opened up broad prospects. Since the 90' s, there are a lot of application problems are more complex object model structure with the correct identification difficult. Now under the MCMC theory, through the use of special statistical software MCMC simulation can solve many complex problems. In addition, thanks to the use of MCMC theory makes Bayesian (Bayes) statistics have been re-revival in the past that were considered impossible calculation of statistical methods is very easy to become a
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下载文件列表
An Introduction to MCMC for Machine Learning
............................................\An Introduction to MCMC for Machine Learning\An Introduction to MCMC for Machine Learning.pdf
............................................\............................................\Monte Carlo.pdf
............................................\An Introduction to MCMC for Machine Learning
............................................\An Introduction to MCMC for Machine Learning\An Introduction to MCMC for Machine Learning.pdf
............................................\............................................\Monte Carlo.pdf
............................................\An Introduction to MCMC for Machine Learning