14 Matching Annotations
  1. Jul 2019
  2. Jan 2019
    1. There are some environmental elements of the Withdrawal Agreement which our current proposals do not cover, namely those concerning the independent body’s scope to enforce implementation of the “non-regression” clause. We will consider these provisions of the Withdrawal Agreement ahead of publishing the final Bill

      hmmmmm....

    2. The text sets out that, if the protocol is required, the UK and EU will not reduce their respective levels of environmental protection below those in place at the end of the implementation period

      note the 'if' attached to N-R

  3. Sep 2018
    1. 生成模型 vs. 判别模型

      总体来看,如果样本足够多,判别模型的正确率高于生成模型的正确率。

      生成模型和判别模型最大的区别在于,生成模型预先假设了很多东西,比如预先假设数据来自高斯,伯努利,符合朴素贝叶斯等等,相当于预先假设了 Hypothesis 函数集,只有在此基础上才有可能求出这个概率分布的参数。

      生成模型,进行了大量脑补。脑补听起来并不是一件好事,但是当你的数据量太小的时候,则必须要求你的模型具备一定的脑补能力。

      判别模型非常依赖样本,他就是很传统,死板,而生成模型比较有想象力,可以“想象”出不存在于当前样本集中的样本,所以他不那么依赖样本。

      关于 想象出不能存在于当前样本集的样本 ,见本课程 40:00 老师举例。

      生成模型在如下情形比判别模型好:

      1. 数据量较小时。
      2. 数据是noisy,标签存在noisy。
      3. 先验概率和类别相关的概率可以统计自不同的来源。

      释疑第三条优点:老师举例,在语音辨识问题中,语音辨识部分虽然是 DNN --- 一个判别模型,但其整体确实一个生成模型,DNN 只是其中一块而已。为什么会这样呢?因为你还是要去算一个先验概率 --- 某一句话被说出来的概率,而获得这个概率并不需要样本一定是声音,只要去网络上爬很多文字对话,就可以估算出这个概率。只有 类别相关的概率 才需要声音和文字pair,才需要判别模型 --- DNN 出马。

  4. Aug 2018
  5. Oct 2017
  6. Jun 2016
    1. The parameter estimate for the first contrast compares the mean of the dependent variable, write, for levels 1 and 2 yielding 11.5417 and is statistically significant (p<.000). The t-value associated with this test is 3.5122.  The results of the second contrast, comparing the mean of write for levels 1 and 3. The expected difference in variable write between group 1 and 3 is 1.7417 and  is not statistically significant (t = 0.6374, p = .5246), while the third contrast is statistically significant. Notice that the intercept corresponds to the cell mean for race = Hispanic group.

      Interpreting the reference group in dummy coding.

  7. Nov 2015
  8. Aug 2015
    1. Quality (DRG) 1.2675 0.61

      It seems there is no statistically significant differences in the Quality measured between the two aggregation levels.

    2. total offive variables in the frontierestimate: three input types in dollars, output in number of patient-days, andquality in estimated HSMR values.

      So this is the model translated as:

      number of patient-days(by hospital*) = HSMR value(ratio) + capital prices($) + labor($) + materials($)

      • all the co-variants are also accounted by Hospital
    3. bootstrap-adjustedTobit regression as specified by Simar and Wilson (2007

      *interesting reference! on "bootstrap-adjusted Tobit regression!

  9. Jan 2015
    1. Logistic regression, also called a logit model, is used to model dichotomous outcome variables. In the logit model the log odds of the outcome is modeled as a linear combination of the predictor variables.