mendicantB

9 ms·

31.
by mendicantB 12y ago
You're still assuming an even spread amongst the ones that are in that 1M. My point is it's usually hard to tell, and you should assume very few sites hold most visitors. Just keep re-applying the concept (of the 1m, 1000 account
32.
by mendicantB 12y ago
I'm pretty sure this is exactly what I just said.
33.
by mendicantB 12y ago
It's dangerous to assume an even spread. Numbers are reported like this because the distribution of site visits is usually confidential information. From experience, I'd guess it's something like 1 million sites holding ~70%
34.
by mendicantB 12y ago
I didn't wish to bring in Bayesian approaches here so as to keep it simple. You are correct in your assertion about likeliness given a priori intuition, and I agree with the linking. But, there is a stark contrast between leveraging pr
35.
by mendicantB 12y ago
Statistics is the means of testing whether or not something is evidence. I understand what you meant, it's an intuitive conclusion, one that doesn't seem like magic. But, something making sense usually does not correlate with or s
36.
by mendicantB 13y ago
You're not the only one. I think that strategy is overzealous and has potential to have terribly unintended consequences. Adria Richards comes to mind.
37.
by mendicantB 13y ago
These algorithms are implemented in just about every major language, as they are standard tools. I'd say your best bet for leveraging them is python scikit learn (super good documentation)
38.
by mendicantB 13y ago
I'm not sure how anyone can take this douche seriously after the leaks.
39.
by mendicantB 13y ago
You disproved your own point. In statistics, σ refers to an area under the normal distribution defined in terms of standard deviations (1σ = 1 standard deviation). In statistics and probability theory, the standard deviation (SD) (represent
40.
by mendicantB 13y ago
It's correct that a normal distribution is assumed in physics, but you are not correct that a standard deviation is defined in terms of a normal distribution.
41.
by mendicantB 13y ago
He's correct. The poster he was responding to made an idiotic statement: "In statistics, σ refers to an area under the normal distribution defined in terms of standard deviations (1σ = 1 standard deviation)." He was just stat
42.
by mendicantB 13y ago
I interviewed there (didn't get an offer, but agreed with the decision, felt too limiting technically). In practice, it's a company wide pissing match that seeks to incubate and accelerate growth for hyper-achievers with hard head
43.
by mendicantB 13y ago
So spot on. Considering recent privacy issues, Github needs to be careful to respond on this. It has potential to explode into an entirely separate shitstorm.
44.
by mendicantB 13y ago
Don't forget to add shake you down for cash vs hit a button on your phone.
45.
by mendicantB 13y ago
Bayesian approaches are probably out of grasp for most small companies. They have a long way to go before being as approachable and easy as frequentist approaches. Schools and the statistics field as a whole need drastic reformation in intr
46.
by mendicantB 13y ago
It's not about developing an alternative, which will likely flounder anyways, considering that WhatsApp has the momentum and critical mass on users already. It's completely a defensive mechanism. WhatsApp dominates and will contin
47.
by mendicantB 13y ago
I felt the same about the YC ecosystem, which is why I submitted this.
48.
Your code may be elegant, but mine works(omniti.com)
164 points by mendicantB 13y ago|168 comments
49.
by mendicantB 13y ago
I'm glad you're being open minded about responses. While I completely agree with you on the massive backlog of problems that could be solved with very simple data analysis (this is the reason I am in the field), your thoughts abou
50.
by mendicantB 13y ago
You are correct that validation sets are only one technique. But, the concept of validation, and the reasoning/justification to do it is an absolutely central idea. What is the point of a model that doesn't aim to generalize?
51.
by mendicantB 13y ago
Honestly, calling validation a trick isn't helping. Understanding the motivation behind validation is an absolutely fundamental concept, and lack of coherence on the topic shows an inherent lack of understanding of the goal of building
52.
by mendicantB 13y ago
My point exactly. There are numerous situations that could doom their product.
53.
by mendicantB 13y ago
Looks like he took your advice. Regardless, I still got the same impression you did.
54.
by mendicantB 13y ago
Sure, in that possibility, that is very true in that nobody could build a full fledged knockoff product. But, what concepts or features that could result in cheap knockoffs? Designed attacks? Password leaks and user privacy breaches? Custom
55.
by mendicantB 13y ago
To be frank, this isn't some minor display bug, he had access your source. In other words, this could have ended your company . He could have sold or leaked it. If naivety is stopping you from grasping the possible consequences, then
56.
by mendicantB 13y ago
Yes it is actually. He pointed out the obvious between the lines message.
57.
aRrgh: A newcomer's angry guide to data types in R(github.com)
10 points by mendicantB 13y ago|0 comments
58.
by mendicantB 13y ago
I think your point is very fair in that the author is exaggerating. But, "highly readable" Are we talking about the same language? The only reason R ever saw use is it's adoption by the statistical community, but all other th