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投稿: 哈密顿蒙特卡洛算法为什么会发散或停止 徐晓鹏 #897
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pull request, equation did not display, original blog: https://statisticalcomputing.github.io/hmc1.html
感谢投稿!请将 PR 题目改为"投稿:标题+作者的形式",如: "投稿: 数据通灵术 杜亚磊"。不知锐哥是否有时间审稿?@zhanruicai |
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请作者先按照以上建议修改。如有问题随时沟通。
- 机器学习 | ||
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本文讨论哈密顿蒙特卡洛算法的稳定性问题及其解决方法。 |
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建议作者加入一段关于哈密顿算法的背景介绍。可以从一个实际的小例子讲起,为什么要用哈密顿算法。哈密顿算法是什么样的?有什么优点?以及本文需要解决的问题是什么。
毕竟是科普系列的文章,我们需要假设读者没有相关的背景知识。
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比如说,相比于MCMC,哈密顿的特点是?
(MCMC的背景也须介绍)
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本文讨论哈密顿蒙特卡洛算法的稳定性问题及其解决方法。 | ||
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这里的主要思想是:一个哈密顿仿真的总能量守恒,则仿真是稳定的。 |
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如何定义能量?稳定是什么意思?
1. 接受概率公式 | ||
2. 能量守恒 | ||
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,我们逐一进行讨论。 |
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这里的算法能否不用图片截图?看起来别扭。
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在统计计算中,我们一般研究第二种情况。这意味着不同位置对应的动能之和不同,对应的动量分布也不同。因此在仿真算法中,如果在不同位置,以相同分布(比如标准正态)生成动量初始值,那么就破坏了能量守恒原则,也违反了统计(力学)的基本假设。 | ||
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受到krauth的statis tical mechanics algorithm and computation的启发,我们采用扔皮球的方式进行讨论。 |
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Typos:
Author name: Werner Krauth.
Book name: Statistical Mechanics: Algorithms and Computations.
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这里的主要思想是:一个哈密顿仿真的总能量守恒,则仿真是稳定的。 | ||
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比如如下这个仿真,红色的是位置-势能轨迹,黑色是位置轨迹,而蓝色是势能(对应概率)的三倍标准差界限。 |
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什么是势能?什么又是势能对应的概率?
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反之,如果总能量不守恒: | ||
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1. 总能量持续变大,则最大势能也会持续变大,导致仿真发散; |
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Same issues. I dont understand what is potential energy.
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此时从位置a扔一个皮球,假设皮球只能落在位置a,b,c,而且皮球将等可能地落在这三个位置上。 | ||
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系统处于a,b等位置的静态概率 |
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这里是在定义静态概率吗?
由于 | ||
$$1-p(a\to a)=p(a \to b)+p(a \to c)$$ | ||
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代入上式,得到 |
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Here it contains a typo in the main paper.
alpha = min(1,np.exp(U(x0) - U(xStar))) | ||
if alpha < rand(): | ||
xStar = x0 | ||
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最后的时候列一下参考文献~
@yufree 作者是不太能看到我上面的评论吗,还是有其他困难 |
我也奇怪,过会我发邮件问下。 |
@yufree 有后续吗 |
邮件当天就发了,但木有回音,我再问问。 |
尊敬的审稿人,
你好,我刚看到回信。你的意见非常有帮助。我会仔细查看后再做修改。谢
谢
zhanruicai <[email protected]> writes:
… @yufree 有后续吗
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Regards,
Xiaopeng
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@yufree 好像没有后续了 |
@statisticalcomputing 有时间修改一下文章吗?主站投稿应从作者最后一次回复后的三个月内处理完成,如果感觉时间不够,到10月6日后我们可以先暂时关闭,待有时间后可联系编辑部重新打开 PR 继续修改。请确认,谢谢! |
作者已联系编辑部,目前比较繁忙,但会有后续修改及投稿 @zhanruicai |
pull request, equation did not display,
original blog: https://statisticalcomputing.github.io/hmc1.html
非常感谢您的PR, 如果您是在为主站投稿, 请将PR的标题改为"投稿:标题+作者的形式",如:
"投稿: 数据通灵术 杜亚磊"
并保留下面的内容.
forum_id
加入文章投稿指南在这里,有任何问题,可以直接在PR留言,其他问题请联系: [email protected]。