g*9
2 楼
EB1B刚刚递上去不久，现在正pending.公司内部有调动，要换到另一个部门，但是是相
似的位置。请问各位前辈，对EB1B会不会有影响？实在不想重新申请。谢谢答复和建议
！
似的位置。请问各位前辈，对EB1B会不会有影响？实在不想重新申请。谢谢答复和建议
！
x*h
3 楼
Why?
A useful and not hard one.
A useful and not hard one.
w*m
4 楼
用的学校的模板，但是pdf出来总是A4的尺寸。省稿的人一定要letter的尺寸，然后
margin也有16左边，11右边的要求。实在搞不定了，请大侠们帮忙指点一下~
我是先生成dvi，然后点dvi to pdf。
拜谢拜谢！！！
margin也有16左边，11右边的要求。实在搞不定了，请大侠们帮忙指点一下~
我是先生成dvi，然后点dvi to pdf。
拜谢拜谢！！！
j*r
5 楼
thx
T*i
6 楼
huh?
【在 x*********h 的大作中提到】
: Why?
: A useful and not hard one.
【在 x*********h 的大作中提到】
: Why?
: A useful and not hard one.
a*e
7 楼
学校模板里应该有纸张选项
你写的是什么，journal paper还是学位论文？
【在 w*******m 的大作中提到】
: 用的学校的模板，但是pdf出来总是A4的尺寸。省稿的人一定要letter的尺寸，然后
: margin也有16左边，11右边的要求。实在搞不定了，请大侠们帮忙指点一下~
: 我是先生成dvi，然后点dvi to pdf。
: 拜谢拜谢！！！
你写的是什么，journal paper还是学位论文？
【在 w*******m 的大作中提到】
: 用的学校的模板，但是pdf出来总是A4的尺寸。省稿的人一定要letter的尺寸，然后
: margin也有16左边，11右边的要求。实在搞不定了，请大侠们帮忙指点一下~
: 我是先生成dvi，然后点dvi to pdf。
: 拜谢拜谢！！！
X*e
8 楼
glm(........ family=binomial(logit))
【在 j**r 的大作中提到】
: thx
【在 j**r 的大作中提到】
: thx
r*y
9 楼
map is a tree - red-black tree
【在 x*********h 的大作中提到】
: Why?
: A useful and not hard one.
【在 x*********h 的大作中提到】
: Why?
: A useful and not hard one.
w*m
10 楼
哈哈，已经搞定了，下载了个新版的latex
然后就可以修改菜单-->Accessories-->MikTeX-->MikTeX Options，把Paper改成
Letter(lettersize)。这样出来的pdf就都是letter size了
另外也谢谢apie~~
然后就可以修改菜单-->Accessories-->MikTeX-->MikTeX Options，把Paper改成
Letter(lettersize)。这样出来的pdf就都是letter size了
另外也谢谢apie~~
p*t
11 楼
如果有bayesian statistic的话，R是不是只能用MCMC来simulate?
【在 X*********e 的大作中提到】
: glm(........ family=binomial(logit))
【在 X*********e 的大作中提到】
: glm(........ family=binomial(logit))
x*h
12 楼
Yeah, I know that, but still not a general simple tree, providing some
walking implementation.
Sometimes, we should try to complete some work by ourselves or resort to
some good codes.
walking implementation.
Sometimes, we should try to complete some work by ourselves or resort to
some good codes.
a*e
13 楼
每次改MikTeX选项麻烦，可以在源文件里声明纸张。
\documentclass[letterpaper]{article}
我问你写的是什么，其实是想知道你的文档类是什么。期刊文章一般
用article，学位论文用book。你们学校的模板未必有纸张选项，所以
我没敢直接建议用上面的方法。
上次我建议某人latex, bibtex, latex, latex
敌人又问：我的编辑器界面上没这个
答：命令行
问：命令行在哪里
答：Start - Run - cmd
问：我用的是中文Windows，没有Start啊
答：[email protected]#$%^&*
【在 w*******m 的大作中提到】
: 哈哈，已经搞定了，下载了个新版的latex
: 然后就可以修改菜单-->Accessories-->MikTeX-->MikTeX Options，把Paper改成
: Letter(lettersize)。这样出来的pdf就都是letter size了
: 另外也谢谢apie~~
\documentclass[letterpaper]{article}
我问你写的是什么，其实是想知道你的文档类是什么。期刊文章一般
用article，学位论文用book。你们学校的模板未必有纸张选项，所以
我没敢直接建议用上面的方法。
上次我建议某人latex, bibtex, latex, latex
敌人又问：我的编辑器界面上没这个
答：命令行
问：命令行在哪里
答：Start - Run - cmd
问：我用的是中文Windows，没有Start啊
答：[email protected]#$%^&*
【在 w*******m 的大作中提到】
: 哈哈，已经搞定了，下载了个新版的latex
: 然后就可以修改菜单-->Accessories-->MikTeX-->MikTeX Options，把Paper改成
: Letter(lettersize)。这样出来的pdf就都是letter size了
: 另外也谢谢apie~~
X*e
14 楼
no, its depend on what is your posterior distribution.
【在 p*******t 的大作中提到】
: 如果有bayesian statistic的话，R是不是只能用MCMC来simulate?
【在 p*******t 的大作中提到】
: 如果有bayesian statistic的话，R是不是只能用MCMC来simulate?
h*e
15 楼
ask on boost mail list.
【在 x*********h 的大作中提到】
: Yeah, I know that, but still not a general simple tree, providing some
: walking implementation.
: Sometimes, we should try to complete some work by ourselves or resort to
: some good codes.
【在 x*********h 的大作中提到】
: Yeah, I know that, but still not a general simple tree, providing some
: walking implementation.
: Sometimes, we should try to complete some work by ourselves or resort to
: some good codes.
S*I
16 楼
此类人还是用Word的好。
【在 a**e 的大作中提到】
: 每次改MikTeX选项麻烦，可以在源文件里声明纸张。
: \documentclass[letterpaper]{article}
: 我问你写的是什么，其实是想知道你的文档类是什么。期刊文章一般
: 用article，学位论文用book。你们学校的模板未必有纸张选项，所以
: 我没敢直接建议用上面的方法。
: 上次我建议某人latex, bibtex, latex, latex
: 敌人又问：我的编辑器界面上没这个
: 答：命令行
: 问：命令行在哪里
: 答：Start - Run - cmd
p*t
17 楼
就是binary choice logit model
顺便问，这个跟probit model的结果有本质性的区别么？
【在 X*********e 的大作中提到】
: no, its depend on what is your posterior distribution.
顺便问，这个跟probit model的结果有本质性的区别么？
【在 X*********e 的大作中提到】
: no, its depend on what is your posterior distribution.
c*l
18 楼
no need
Everybody builds its own tree
【在 x*********h 的大作中提到】
: Why?
: A useful and not hard one.
Everybody builds its own tree
【在 x*********h 的大作中提到】
: Why?
: A useful and not hard one.
X*e
19 楼
hehe, sorry,i don't know
knew very few about probit model
【在 p*******t 的大作中提到】
: 就是binary choice logit model
: 顺便问，这个跟probit model的结果有本质性的区别么？
knew very few about probit model
【在 p*******t 的大作中提到】
: 就是binary choice logit model
: 顺便问，这个跟probit model的结果有本质性的区别么？
x*4
20 楼
logit assumes the error term follows log normal distribution which cannot be
negative, while probit assumes the error term follows normal distribution
that can be negative......
【在 p*******t 的大作中提到】
: 就是binary choice logit model
: 顺便问，这个跟probit model的结果有本质性的区别么？
negative, while probit assumes the error term follows normal distribution
that can be negative......
【在 p*******t 的大作中提到】
: 就是binary choice logit model
: 顺便问，这个跟probit model的结果有本质性的区别么？
p*t
21 楼
我知道.......所以我想问又没有本质的区别.....比如substitution pattern之类的
be
【在 x********4 的大作中提到】
: logit assumes the error term follows log normal distribution which cannot be
: negative, while probit assumes the error term follows normal distribution
: that can be negative......
be
【在 x********4 的大作中提到】
: logit assumes the error term follows log normal distribution which cannot be
: negative, while probit assumes the error term follows normal distribution
: that can be negative......
j*r
22 楼
gotcha!
thanks
【在 X*********e 的大作中提到】
: hehe, sorry,i don't know
: knew very few about probit model
thanks
【在 X*********e 的大作中提到】
: hehe, sorry,i don't know
: knew very few about probit model
t*g
23 楼
Wrong. Of course logit error term can be negative. We assume that the
distribution of error term in logit model is logistic.
be
【在 x********4 的大作中提到】
: logit assumes the error term follows log normal distribution which cannot be
: negative, while probit assumes the error term follows normal distribution
: that can be negative......
distribution of error term in logit model is logistic.
be
【在 x********4 的大作中提到】
: logit assumes the error term follows log normal distribution which cannot be
: negative, while probit assumes the error term follows normal distribution
: that can be negative......
t*g
24 楼
No big difference between logit and probit, however, you choose one of them
based on your preference. For instance, it may make your life easier to
choose probit, as its error is normal, which can either fit your model
better or make your presentation more reasonable for people outside
econometrics(you know, sometimes it is hard to explain to dumb guys why you
need a logistic distribution instead of normal). Historically, logit was
thought easier to compute during the period when computing distr
【在 p*******t 的大作中提到】
: 我知道.......所以我想问又没有本质的区别.....比如substitution pattern之类的
:
: be
based on your preference. For instance, it may make your life easier to
choose probit, as its error is normal, which can either fit your model
better or make your presentation more reasonable for people outside
econometrics(you know, sometimes it is hard to explain to dumb guys why you
need a logistic distribution instead of normal). Historically, logit was
thought easier to compute during the period when computing distr
【在 p*******t 的大作中提到】
: 我知道.......所以我想问又没有本质的区别.....比如substitution pattern之类的
:
: be
p*t
25 楼
知道了，谢谢啦
them
you
,
【在 t****g 的大作中提到】
: No big difference between logit and probit, however, you choose one of them
: based on your preference. For instance, it may make your life easier to
: choose probit, as its error is normal, which can either fit your model
: better or make your presentation more reasonable for people outside
: econometrics(you know, sometimes it is hard to explain to dumb guys why you
: need a logistic distribution instead of normal). Historically, logit was
: thought easier to compute during the period when computing distr
them
you
,
【在 t****g 的大作中提到】
: No big difference between logit and probit, however, you choose one of them
: based on your preference. For instance, it may make your life easier to
: choose probit, as its error is normal, which can either fit your model
: better or make your presentation more reasonable for people outside
: econometrics(you know, sometimes it is hard to explain to dumb guys why you
: need a logistic distribution instead of normal). Historically, logit was
: thought easier to compute during the period when computing distr
f*r
26 楼
Generally there are two ways of deriving the models for binary data:
1. latent variable approach: y*=x \beta + u. Here y* is a continuous
latent variable. then u can be treated as logistically or normally
distributed, which corresponds to logit or probit model. This approach is
modeling the underline variable behind the binary variable y.
2. link function approach. This approach models the binary variable y
directly: y=g(X \beta), where g() is the inverse link function. Basically
it maps
1. latent variable approach: y*=x \beta + u. Here y* is a continuous
latent variable. then u can be treated as logistically or normally
distributed, which corresponds to logit or probit model. This approach is
modeling the underline variable behind the binary variable y.
2. link function approach. This approach models the binary variable y
directly: y=g(X \beta), where g() is the inverse link function. Basically
it maps
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