on 10-May-2018 (Thu)

Je voudrais de la lumière
Comme en Nouvelle-Angleterre
Je veux changer d'atmosphère
Dans mon jardin d'hiver

Ta robe à fleur
Sous la pluie de novembre
Mes mains qui courent
Je n'en peux plus de t'attendre
Les années passent
Qu'il est loin l'âge tendre
Nul ne peut nous entendre

Je voudrais du Fred Astaire
Revoir un Latécoère
Je voudrais toujours te plaire
Dans mon jardin d'hiver

Je veux déjeuner par terre
Comme au long des golfes clairs
T'embrasser les yeux ouverts
Dans mon jardin d'hiver

Remember that using Bayes' Theorem doesn't make you a Bayesian. Quantifying uncertainty with probability makes you a Bayesian.

It’s more helpful to think about exploitation and disrespect than to define cultural “ownership”

Currently, the stan repo includes the language, the algorithms, and the service API for the interfaces.

Your model is only as good as your ability to communicate it to others, and communication is drastically improved by treating your Stan program as a piece of software and applying the best practices from the software development community.

Ideally, your analysis will be broken up into a Stan program, a data file, possibly an init file, and scripts that process the analysis in your favorite environment.

Data "corrections" such as background subtraction and deconvolution censor information in the data and limit how much you can learn in a given experiment

modeling the data generatively as a sum of signal plus background or signal smeared with measurement variability allows you to build an accurate model capable to extracting as much information from the data as possible.

Instead of trying to model every detail of the experiment immediately, begin with a simple approximation that captures the gross features of the data generating process.

One of the most powerful means of validating a statistical algorithm is to verify that you can recover the ground truth from simulated data.

A nice consistency check is whether or not the 5% to 95% marginal posterior interval captures each of the "true" parameter values.

The best generic diagnostic for the accuracy of a Markov chain Monte Carlo algorithm is the split R-hat statistic which quantifies the consistency of an ensemble of Markov chains.

Slow chains lead to low numbers of effective samples, and if there are too few effective samples then we can't accurate estimate the number of effective samples at all.

The Folk Theorem of Statistical Computing is a heuristic developed by Andrew Gelman stating that most statistical computational problems are due not to the algorithm being used but rather the model itself.

The Folk Theorem is an important perspective because if forces the user to confront and evaluate the assumptions inherent to their model.

Some principles we don't like: invariance, Jeffreys, entropy

如何自己购买印度“救命药”（已上图）附加福利

spinit();

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右下角点击一下就可以直接和客服对话了
可能中国市场很大他们也会用翻译软件跟你中文交流 所以基本是没有障碍的
英文好的可以直接打电话过去联系
另外阿波罗官网药房是可以还价的，很奇葩的一个国家。
很多人不知道什么救命药，
比如治愈丙肝的吉利德，癌症肿瘤进口靶向药，这些在印度都是买到的。
价格悬殊可以说想多大就有多大
新人1024回复很慢 有需要了解更多的可以私信我留下VX
不知道留站外网址有没有违规
如有违规请版主及时删贴
另外给一波福利，印度的西药伟哥之前很多大神都介绍过
告诉大家几款印度的草本伟哥，不含西药成分的。
这几个亲试有效的，其实我们老祖宗以前应该也有很多的，要不古代那些皇帝怎么雨露均沾
真的不知道那些配方都遗落到哪里去了

• null

游客多年 终得一码 非常感谢
没啥经验，就分享一些自己的作为印度代购的一些经验
之前看过百舸大神介绍的印度伟哥类很详细，就不多做介绍了
一个合格的印度代购主要是做什么，当然是做药了。
印度的药为什么便宜
1、政府保护仿制，充当“推销员”
1970年印度总理英迪拉·甘地主导修订了的《专利法》规定，对食品、药品只授予工艺专利，不授予产品专利，这意味着印度放弃了对药品化合物的知识产权保护，
制度上的宽松使得本国企业能够获得大量仿制药生产许可，从而为印度仿制药提供了快速扩张的空间。

2、人力成本低，技术雄厚
印度制药可谓占尽天时地利人和，制造业成本低、操作技术简单、劳动强度大，使得印度各个种类的制造业发展迅速，也逐渐积攒了雄厚的技术。
目前印度境内拥有FDA认证的药厂共有119家，可向美国出口约900种获得FDA批准的药物和制药原料；拥有英国药品管理局认证的药厂也已达80多家。

我给大家介绍的是如何购买印度这些救命药，可能这个方法会断了一些人的财路，但也希望更多人能吃得起吧
很多人可能也知道印度药，也找代购买过，有些医院的主治医生也都在推销，不得不说这是一个很大的利益链吧
其实这些要完全可以自己购买，价格上面肯定会优惠一些，毕竟只要倒手就要加价，不赚钱别人怎么生活，无可厚非吧
可恨的是那些骗子代购，骗这个钱真的是禽兽不如，对于需要这些药的家庭来说真的是雪上加霜

首先如何购买
印度大的药房有两家，阿波罗和罗氏，他们都是有网上药房的
以阿波罗举例：在阿波罗药房买药最大的难点不是语言问题，只要你会用电脑会上网，开个翻译软件和他们聊好了，他们不会不耐烦。虽然翻译软件翻译的可能不太准确，
但是大概的意思不会错的，彼此之间都能明白别人的意思，或者身边有英文水平高的朋友也可以请他帮忙。最大的难点就在信用卡上，必须要有VISA卡，
还要到银行去开通国际支付功能，还要有最少1万的额度。

进去阿波罗药房首页后点击右下角的“online”那里和工作人员沟通，告诉他你要什么和数量，跟他谈好价钱，然后他就会要求你提供医生开的处方药，一般我们的处方药是中文的，
只要用英文翻译好名字和药名和数量就可以了，提供处方药给他后（这个淘宝可以找某宝人工翻译），3个小时内他就会给你一个下单链接，付款之后他就会给你发货了，新手不会上图。
等学会的给大家上图演示一下

有几点需要注意下
1.收货人请填写病人姓名，手机号无所谓，因为一旦被扣可以凭处方，诊断书去海关拿药。只有极个别城市是没有人情的，大多数省份都是可以拿出来的，但请一次不要购买太多，有倒卖嫌疑
2，广西，福建，四川，江苏，山东（据说有个重要的会议在六月份），这几个地方是经常扣货的，广西，四川几乎见一个扣一个，
但都可以拿到，如果资料齐全的话，最好的找一个其他地区的亲戚，朋友，同学代收，转给你。
至于网址大家可以自行百度，另外如果请代购，请大家一定要担保交易，现在假的太多，不要相信什么违禁品不能担保交易，不管是国家还是某宝，都是处于一种模糊的态度，只是很多人不了解罢了
第一次发帖，胡乱写写，有什么问题可以私信我。我也是代购印度产品的，但不做这些“救命药”，做什么你们猜吧

When you type any command at the prompt (say, python), the system has a well-defined sequence of places that it looks for the executable. This sequence is defined in a system variable called PATH, which the user can specify. To see your PATH, you can type echo $PATH.

The result is a list of directories on your computer, which will be searched in order for the desired executable.

When you run python and do something like import matplotlib, Python has to play a similar game to find the package you have in mind. Similar to $PATH in unix, Python has sys.path that specifies these

by default, the first entry in sys.path is the current directory.

is the current directory. Also, unless you modify this (which you shouldn't do unless you know exactly what you're doing) you'll usually find something called site-packages in the path: this is the default place that Python puts packages when you install them using python setup.py install, or pip, or conda, or a similar means.

some Python packages come bundled with stand-alone scripts that you can run from the command line (examples are pip, ipython, jupyter, pep8, etc.) By default, these executables will be put in the same directory path as the Python used to install them, and are designed to work only with that Python installation.

Well, first make sure your $PATH variable is doing what you want it to. You likely have a startup script called something like ~/.bash_profile or ~/.bashrc that sets this $PATH variable.

But in order for this to happen, Jupyter needs to know where to look for the associated executable: that is, it needs to know which path the python sits in.

These paths are specified in jupyter's kernelspec, and it's possible for the user to adjust them to their desires.

here's the list of kernels that I have on my system:

 $ jupyter kernelspec list Available kernels : python2 . 7 / Users / jakevdp /. ipython / kernels / python2 . 7 python3 . 3 / Users / jakevdp /. ipython / kernels / python3 . 3 python3 . 4 / Users / jakevdp /. ipython / kernels / python3 . 4 python3 . 5 / Users / jakevdp /. ipython / kernels / python3 . 5 python2 / Users / jakevdp / Library / Jupyter / kernels / python2 python3 / Users / jakevdp / Library / Jupyter / kernels / python3 

Each of these is a directory containing some metadata that specifies the kernel name, the path to the executable, and other relevant info.

IPython relies on the ipykernel package which contains a command to install a python kernel: for example

 $ python - m ipykernel install 

It will create a kernelspec associated with the Python executable you use to run this command. You can then choose this kernel in the Jupyter notebook to run your code with that Python.