🕷️ Crawler Inspector

URL Lookup

Direct Parameter Lookup

Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 50 (from laksa168)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa050.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://cloud.tencent.com/developer/article/1639197\')), getAhrefsUnparsedNoserviceFromURL(\'https://cloud.tencent.com/developer/article/1639197\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/cloud.tencent.com\/developer\/article\/1639197","crawl_time":1757983659,"first_indexed_time":1666892124,"http_code":200,"src_unparsed":"com,tencent!cloud,\/developer\/article\/1639197 s443","src_root_hash":"6417570461941030050","history_drop_reason":null,"meta_title":"最大似然估计（MLE）原理及计算方法-腾讯云开发者社区-腾讯云","meta_descriptions":["1）有两堆球，其中A堆有99个白球和1个黑球，B堆有99个黑球和1个白球。假如随便摸一个球，发现是黑球，那么这个球更有可能来自于哪一堆？"],"attrs_boilerpipe_text":"1. 实际例子 1）有两堆球，其中A堆有99个白球和1个黑球，B堆有99个黑球和1个白球。假如随便摸一个球，发现是黑球，那么这个球更有可能来自于哪一堆？ 2）猎人和徒弟去打猎，打倒了一只兔子，更可能是谁打中的？ 3）一堆球，里边有黑白两色的球，其中一种颜色的有90个，另一种颜色的有10个。如果摸出一个球是黑色的，那么里边哪种颜色有90个？ 第一个肯定觉得是来自B堆，第二个中更可能是师傅，第三个里边黑球有90个。我们的估计基于，概率最高的事情，更可能发生。一次实验就出现的事件，这件事有较大的概率发生。 2.  数学表述 最大似然估计这个名字是由高斯先提出，Fisher后来重新提出并证明了一些特征。这是统计学中的常用方法，机器学习中的逻辑回归中也是基于它计算的损失函数。 当样本分布是离散型： 当样本分布为连续型时： 一般情况下求估计值的步骤：\n 1）构造似然函数?(?)\n 2）取对数：???(?)  似然函数是连乘，不好求导；取对数后可化为加法，求导方便。\n 3）求导，计算极值\n 4）解方程，得到? 如果似然方程无解，或者似然函数不可导，则需要考虑其他方法。 3. 计算实例 （此题来自于 https:\/\/wenku.baidu.com\/view\/0d9af6aa172ded630b1cb69a.html ）\n  参考：\n 1） https:\/\/wenku.baidu.com\/view\/0d9af6aa172ded630b1cb69a.html \n 2） https:\/\/medium.com\/@rrfd\/what-is-maximum-likelihood-estimation-examples-in-python-791153818030 、","attrs_markdown":"[生信编程日常](https:\/\/cloud.tencent.com\/developer\/user\/7144248)\n\n## 最大似然估计（MLE）原理及计算方法\n\n关注作者\n\n[*腾讯云*](https:\/\/cloud.tencent.com\/?from=20060&from_column=20060)\n\n[*开发者社区*](https:\/\/cloud.tencent.com\/developer)\n\n[文档](https:\/\/cloud.tencent.com\/document\/product?from=20702&from_column=20702)[建议反馈](https:\/\/cloud.tencent.com\/voc\/?from=20703&from_column=20703)[控制台](https:\/\/console.cloud.tencent.com\/?from=20063&from_column=20063)\n\n登录\/注册\n\n[首页](https:\/\/cloud.tencent.com\/developer)\n\n学习\n\n活动\n\n专区\n\n圈层\n\n工具\n\n[MCP广场![](https:\/\/qccommunity.qcloudimg.com\/image\/new.png)](https:\/\/cloud.tencent.com\/developer\/mcp)\n\n文章\/答案\/技术大牛\n\n搜索\n\n搜索\n\n关闭\n\n发布\n\n[首页](https:\/\/cloud.tencent.com\/developer)\n\n学习\n\n活动\n\n专区\n\n圈层\n\n工具\n\n[MCP广场![](https:\/\/qccommunity.qcloudimg.com\/image\/new.png)](https:\/\/cloud.tencent.com\/developer\/mcp)\n\n[返回腾讯云官网](https:\/\/cloud.tencent.com\/?from=20060&from_column=20060)\n\n生信编程日常\n\n[首页](https:\/\/cloud.tencent.com\/developer)\n\n学习\n\n活动\n\n专区\n\n圈层\n\n工具\n\n[MCP广场![](https:\/\/qccommunity.qcloudimg.com\/image\/new.png)](https:\/\/cloud.tencent.com\/developer\/mcp)\n\n[返回腾讯云官网](https:\/\/cloud.tencent.com\/?from=20060&from_column=20060)\n\n[社区首页](https:\/\/cloud.tencent.com\/developer) \\>[专栏](https:\/\/cloud.tencent.com\/developer\/column) \\>最大似然估计（MLE）原理及计算方法\n\n# 最大似然估计（MLE）原理及计算方法\n![作者头像](https:\/\/ask.qcloudimg.com\/http-save\/yehe-7144248\/64cmtrzzq0.jpeg)\n\n生信编程日常\n\n关注\n\n发布于 2020-06-05 11:04:30\n\n发布于 2020-06-05 11:04:30\n\n8\\.7K\n\n0\n\n举报\n\n文章被收录于专栏：[生物信息学、python、R、linux](https:\/\/cloud.tencent.com\/developer\/column\/85939)[生物信息学、python、R、linux]()\n\n###### 1\\. 实际例子\n1）有两堆球，其中A堆有99个白球和1个黑球，B堆有99个黑球和1个白球。假如随便摸一个球，发现是黑球，那么这个球更有可能来自于哪一堆？\n\n2）猎人和徒弟去打猎，打倒了一只兔子，更可能是谁打中的？\n\n3）一堆球，里边有黑白两色的球，其中一种颜色的有90个，另一种颜色的有10个。如果摸出一个球是黑色的，那么里边哪种颜色有90个？\n\n第一个肯定觉得是来自B堆，第二个中更可能是师傅，第三个里边黑球有90个。我们的估计基于，概率最高的事情，更可能发生。一次实验就出现的事件，这件事有较大的概率发生。\n\n###### 2\\. 数学表述\n最大似然估计这个名字是由高斯先提出，Fisher后来重新提出并证明了一些特征。这是统计学中的常用方法，机器学习中的逻辑回归中也是基于它计算的损失函数。\n\n当样本分布是离散型：\n\n![](https:\/\/ask.qcloudimg.com\/http-save\/6869393\/bk9772is9f.png)\n\n当样本分布为连续型时：\n\n![](https:\/\/ask.qcloudimg.com\/http-save\/6869393\/jyd5jolo4p.png)\n\n一般情况下求估计值的步骤： 1）构造似然函数?(?) 2）取对数：???(?) 似然函数是连乘，不好求导；取对数后可化为加法，求导方便。 3）求导，计算极值 4）解方程，得到?\n\n如果似然方程无解，或者似然函数不可导，则需要考虑其他方法。\n\n###### 3\\. 计算实例\n（此题来自于[https:\/\/wenku.baidu.com\/view\/0d9af6aa172ded630b1cb69a.html](https:\/\/cloud.tencent.com\/developer\/tools\/blog-entry?target=https%3A%2F%2Flinks.jianshu.com%2Fgo%3Fto%3Dhttps%253A%252F%252Fwenku.baidu.com%252Fview%252F0d9af6aa172ded630b1cb69a.html&objectId=1639197&objectType=1&contentType=undefined)）\n\n![](https:\/\/ask.qcloudimg.com\/http-save\/6869393\/8enqplfloo.png)\n\n![](https:\/\/ask.qcloudimg.com\/http-save\/6869393\/8a2vdfxq86.png)\n\n![](https:\/\/ask.qcloudimg.com\/http-save\/6869393\/90xzq9miud.png)\n\n参考： 1）[https:\/\/wenku.baidu.com\/view\/0d9af6aa172ded630b1cb69a.html](https:\/\/cloud.tencent.com\/developer\/tools\/blog-entry?target=https%3A%2F%2Flinks.jianshu.com%2Fgo%3Fto%3Dhttps%253A%252F%252Fwenku.baidu.com%252Fview%252F0d9af6aa172ded630b1cb69a.html&objectId=1639197&objectType=1&contentType=undefined) 2）[https:\/\/medium.com\/@rrfd\/what-is-maximum-likelihood-estimation-examples-in-python-791153818030](https:\/\/cloud.tencent.com\/developer\/tools\/blog-entry?target=https%3A%2F%2Flinks.jianshu.com%2Fgo%3Fto%3Dhttps%253A%252F%252Fmedium.com%252F%2540rrfd%252Fwhat-is-maximum-likelihood-estimation-examples-in-python-791153818030&objectId=1639197&objectType=1&contentType=undefined)\n\n、\n\n本文参与 [腾讯云自媒体同步曝光计划](https:\/\/cloud.tencent.com\/developer\/support-plan)，分享自作者个人站点\/博客。\n\n如有侵权请联系 [cloudcommunity@tencent.com](mailto:cloudcommunity@tencent.com) 删除\n\n前往查看\n\n[https](https:\/\/cloud.tencent.com\/developer\/tag\/10813)\n\n[网络安全](https:\/\/cloud.tencent.com\/developer\/tag\/10681)\n\n[html](https:\/\/cloud.tencent.com\/developer\/tag\/10205)\n\n本文分享自 作者个人站点\/博客 前往查看\n\n如有侵权，请联系 [cloudcommunity@tencent.com](mailto:cloudcommunity@tencent.com) 删除。\n\n本文参与 [腾讯云自媒体同步曝光计划](https:\/\/cloud.tencent.com\/developer\/support-plan) 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3. Robots.txt Check

Query:

Response:

4. Spam/Ban Check

Query:

Response:

5. Seen Status Check

ℹ️ Skipped - page is already crawled

🚫

NOT INDEXABLE

✅

CRAWLED

6 months ago

🤖

ROBOTS ALLOWED

Page Info Filters

Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
Age cutoff	FAIL	`download_stamp > now() - 6 MONTH`	6.9 months ago
History drop	PASS	`isNull(history_drop_reason)`	No drop reason
Spam/ban	PASS	`fh_dont_index != 1 AND ml_spam_score = 0`	ml_spam_score=0
Canonical	PASS	`meta_canonical IS NULL OR = '' OR = src_unparsed`	Not set

Page Details

Property	Value
URL	https://cloud.tencent.com/developer/article/1639197
Last Crawled	2025-09-16 00:47:39 (6 months ago)
First Indexed	2022-10-27 17:35:24 (3 years ago)
HTTP Status Code	200
Meta Title	最大似然估计（MLE）原理及计算方法-腾讯云开发者社区-腾讯云
Meta Description	1）有两堆球，其中A堆有99个白球和1个黑球，B堆有99个黑球和1个白球。假如随便摸一个球，发现是黑球，那么这个球更有可能来自于哪一堆？
Meta Canonical	null
Boilerpipe Text	1. 实际例子 1）有两堆球，其中A堆有99个白球和1个黑球，B堆有99个黑球和1个白球。假如随便摸一个球，发现是黑球，那么这个球更有可能来自于哪一堆？ 2）猎人和徒弟去打猎，打倒了一只兔子，更可能是谁打中的？ 3）一堆球，里边有黑白两色的球，其中一种颜色的有90个，另一种颜色的有10个。如果摸出一个球是黑色的，那么里边哪种颜色有90个？第一个肯定觉得是来自B堆，第二个中更可能是师傅，第三个里边黑球有90个。我们的估计基于，概率最高的事情，更可能发生。一次实验就出现的事件，这件事有较大的概率发生。 2. 数学表述最大似然估计这个名字是由高斯先提出，Fisher后来重新提出并证明了一些特征。这是统计学中的常用方法，机器学习中的逻辑回归中也是基于它计算的损失函数。当样本分布是离散型：当样本分布为连续型时：一般情况下求估计值的步骤： 1）构造似然函数?(?) 2）取对数：???(?) 似然函数是连乘，不好求导；取对数后可化为加法，求导方便。 3）求导，计算极值 4）解方程，得到? 如果似然方程无解，或者似然函数不可导，则需要考虑其他方法。 3. 计算实例（此题来自于 https://wenku.baidu.com/view/0d9af6aa172ded630b1cb69a.html ）参考： 1） https://wenku.baidu.com/view/0d9af6aa172ded630b1cb69a.html 2） https://medium.com/@rrfd/what-is-maximum-likelihood-estimation-examples-in-python-791153818030 、
Markdown	[生信编程日常](https://cloud.tencent.com/developer/user/7144248) ## 最大似然估计（MLE）原理及计算方法关注作者 [腾讯云](https://cloud.tencent.com/?from=20060&from_column=20060) [开发者社区](https://cloud.tencent.com/developer) [文档](https://cloud.tencent.com/document/product?from=20702&from_column=20702)[建议反馈](https://cloud.tencent.com/voc/?from=20703&from_column=20703)[控制台](https://console.cloud.tencent.com/?from=20063&from_column=20063) 登录/注册 [首页](https://cloud.tencent.com/developer) 学习活动专区圈层工具 [MCP广场![](https://qccommunity.qcloudimg.com/image/new.png)](https://cloud.tencent.com/developer/mcp) 文章/答案/技术大牛搜索搜索关闭发布 [首页](https://cloud.tencent.com/developer) 学习活动专区圈层工具 [MCP广场![](https://qccommunity.qcloudimg.com/image/new.png)](https://cloud.tencent.com/developer/mcp) [返回腾讯云官网](https://cloud.tencent.com/?from=20060&from_column=20060) 生信编程日常 [首页](https://cloud.tencent.com/developer) 学习活动专区圈层工具 [MCP广场![](https://qccommunity.qcloudimg.com/image/new.png)](https://cloud.tencent.com/developer/mcp) [返回腾讯云官网](https://cloud.tencent.com/?from=20060&from_column=20060) [社区首页](https://cloud.tencent.com/developer) \>[专栏](https://cloud.tencent.com/developer/column) \>最大似然估计（MLE）原理及计算方法 # 最大似然估计（MLE）原理及计算方法 ![作者头像](https://ask.qcloudimg.com/http-save/yehe-7144248/64cmtrzzq0.jpeg) 生信编程日常关注发布于 2020-06-05 11:04:30 发布于 2020-06-05 11:04:30 8\.7K 0 举报文章被收录于专栏：[生物信息学、python、R、linux](https://cloud.tencent.com/developer/column/85939)[生物信息学、python、R、linux]() ###### 1\. 实际例子 1）有两堆球，其中A堆有99个白球和1个黑球，B堆有99个黑球和1个白球。假如随便摸一个球，发现是黑球，那么这个球更有可能来自于哪一堆？ 2）猎人和徒弟去打猎，打倒了一只兔子，更可能是谁打中的？ 3）一堆球，里边有黑白两色的球，其中一种颜色的有90个，另一种颜色的有10个。如果摸出一个球是黑色的，那么里边哪种颜色有90个？第一个肯定觉得是来自B堆，第二个中更可能是师傅，第三个里边黑球有90个。我们的估计基于，概率最高的事情，更可能发生。一次实验就出现的事件，这件事有较大的概率发生。 ###### 2\. 数学表述最大似然估计这个名字是由高斯先提出，Fisher后来重新提出并证明了一些特征。这是统计学中的常用方法，机器学习中的逻辑回归中也是基于它计算的损失函数。当样本分布是离散型： ![](https://ask.qcloudimg.com/http-save/6869393/bk9772is9f.png) 当样本分布为连续型时： ![](https://ask.qcloudimg.com/http-save/6869393/jyd5jolo4p.png) 一般情况下求估计值的步骤： 1）构造似然函数?(?) 2）取对数：???(?) 似然函数是连乘，不好求导；取对数后可化为加法，求导方便。 3）求导，计算极值 4）解方程，得到? 如果似然方程无解，或者似然函数不可导，则需要考虑其他方法。 ###### 3\. 计算实例（此题来自于[https://wenku.baidu.com/view/0d9af6aa172ded630b1cb69a.html](https://cloud.tencent.com/developer/tools/blog-entry?target=https%3A%2F%2Flinks.jianshu.com%2Fgo%3Fto%3Dhttps%253A%252F%252Fwenku.baidu.com%252Fview%252F0d9af6aa172ded630b1cb69a.html&objectId=1639197&objectType=1&contentType=undefined)） ![](https://ask.qcloudimg.com/http-save/6869393/8enqplfloo.png) ![](https://ask.qcloudimg.com/http-save/6869393/8a2vdfxq86.png) ![](https://ask.qcloudimg.com/http-save/6869393/90xzq9miud.png) 参考： 1）[https://wenku.baidu.com/view/0d9af6aa172ded630b1cb69a.html](https://cloud.tencent.com/developer/tools/blog-entry?target=https%3A%2F%2Flinks.jianshu.com%2Fgo%3Fto%3Dhttps%253A%252F%252Fwenku.baidu.com%252Fview%252F0d9af6aa172ded630b1cb69a.html&objectId=1639197&objectType=1&contentType=undefined) 2）[https://medium.com/@rrfd/what-is-maximum-likelihood-estimation-examples-in-python-791153818030](https://cloud.tencent.com/developer/tools/blog-entry?target=https%3A%2F%2Flinks.jianshu.com%2Fgo%3Fto%3Dhttps%253A%252F%252Fmedium.com%252F%2540rrfd%252Fwhat-is-maximum-likelihood-estimation-examples-in-python-791153818030&objectId=1639197&objectType=1&contentType=undefined) 、本文参与 [腾讯云自媒体同步曝光计划](https://cloud.tencent.com/developer/support-plan)，分享自作者个人站点/博客。如有侵权请联系 [cloudcommunity@tencent.com](mailto:cloudcommunity@tencent.com) 删除前往查看 [https](https://cloud.tencent.com/developer/tag/10813) [网络安全](https://cloud.tencent.com/developer/tag/10681) [html](https://cloud.tencent.com/developer/tag/10205) 本文分享自作者个人站点/博客前往查看如有侵权，请联系 [cloudcommunity@tencent.com](mailto:cloudcommunity@tencent.com) 删除。本文参与 [腾讯云自媒体同步曝光计划](https://cloud.tencent.com/developer/support-plan) ，欢迎热爱写作的你一起参与！ [https](https://cloud.tencent.com/developer/tag/10813) [网络安全](https://cloud.tencent.com/developer/tag/10681) [html](https://cloud.tencent.com/developer/tag/10205) 评论登录后参与评论 0 条评论热度最新登录后参与评论推荐阅读目录 - 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