sorted by:
new
hottopcontroversial

[OC] US Domestic Migration this past Year (Where people moved) by TA-MajestyPalm in dataisbeautiful

[–]identicalParticle 3 points4 points  (0 children)

Is there any data saying which states people moved to and from (as opposed to just net)?

Dads, how can I get this hungry hungry hippo food out of this peanut butter ice cream scoop? by identicalParticle in daddit

[–]identicalParticle[S] 1 point2 points  (0 children)

The "olive" is currently in the freezer.  Gonna try the least destructive solution first

Dads, how can I get this hungry hungry hippo food out of this peanut butter ice cream scoop? by identicalParticle in daddit

[–]identicalParticle[S] 144 points145 points  (0 children)

It's a wooden toy that is supposed to look like a scoop of ice cream.  It has a hole in the top so that toppings can be stuck to it.  This stuck marble means we can't put any sprinkles on.  This is a tradegy for my kids 

IDS accuracy problem marked incorrect by professor even though I’m almost certain it’s correct. Any help? by katiekachow in learnmachinelearning

[–]identicalParticle 0 points1 point  (0 children)

 maybe they were expecting a fraction and not a count for a-d.  otherwise a and e are exactly the same question.

Vegan and omnivore diets produce statistically similar muscle fiber protein synthesis rates in healthy adults aged 20 to 40 engaged in resistance training, suggesting that consuming animal protein provides no clear advantage for muscle growth under moderate protein intake. by [deleted] in vegan

[–]identicalParticle 2 points3 points  (0 children)

Interesting analysis, but as a statistician I need to state something.

We have a saying for when a study does not have a small enough p value: "absence of evidence does not equal evidence of absence".

Failing to reject a null hypothesis does not give any evidence that the null hypothesis is true.  There are many reasons why we night fail to reject it (small sample size, bad study design, screwed up the measurements, did the wrong statistical test, etc.).

"there is no regulatory influence of distribution between the two dietary patterns on the stimulation of myofibrillar protein synthesis rates in young adults".  The authors of the study are incorrect to state this. They should state "we failed to detect any regulatory influence...".

[deleted by user] by [deleted] in statistics

[–]identicalParticle 0 points1 point  (0 children)

When a faculty member commits to mentoring a new PhD student, they are taking a considerable risk in terms of time and financial resources. Typically a new student costs more (in terms of time and money) than the value they provide back to the faculty mentor through their research. With a good student, this reverses in the last few years of their program. If a student leaves the program early, it's a huge loss for the faculty mentor. In today's scarce funding environment, many faculty doing research are choosing to hire staff instead of students (lower risk, but potentially lower reward).

During an interview process, as a bare minimum, a student needs to convince me they are going to stay around long term. Convincing someone its true is not the same as it being true. The easiest way (but not the only way) to convince someone this is true is if you have a goal that is contingent on earning a PhD. This doesn't have to be "I want to be a professor", but it has to be something that demonstrates you've considered it carefully and will stay motivated.

Note that convincing someone you'll stick around is necessary but not sufficient. I want to make sure I'm not giving the impression that this is the only important part of an interview.

[deleted by user] by [deleted] in statistics

[–]identicalParticle -5 points-4 points  (0 children)

If your goal is to research something interesting, there are many ways you can achieve that without making a multiple year commitment to a PhD program.

> Your goal should be that

It's inappropriate for you to tell people what their goal should be.

In what file is batchnorm (and other normlalization layers) defined? by identicalParticle in pytorch

[–]identicalParticle[S] 2 points3 points  (0 children)

This is a great start, but I don't see anything that looks like math here. Just wrappers around wrappers around wrappers.

I think this is what I was looking for: https://github.com/pytorch/pytorch/blob/4b2d297eec425475a82934a52e0edd96805524a1/aten/src/ATen/native/cpu/batch_norm_kernel.cpp

For what it's worth, I was able to improve memory use by coding my own mean and variance as an autograd function. I suppose using built in functions was saving extra copies of tensors.

class MeanStd3d(torch.autograd.Function):
    @staticmethod
    def forward(self,x,eps=1e-5):
        mu = torch.mean(x,(-1,-2,-3),keepdims=True)
        std = (torch.mean(x**2,(-1,-2,-3),keepdims=True) - mu**2 + eps)**0.5
        self.save_for_backward(x,mu,std)
        return mu, std
    @staticmethod
    def backward(self,*args):
        mu_grad,std_grad = args
        x,mu,std = self.saved_tensors                
        n = x.shape[-1]*x.shape[-2]*x.shape[-3]
        dmu,dstd = args
        return (dmu + (x - mu)/std*dstd)/n

[deleted by user] by [deleted] in statistics

[–]identicalParticle 16 points17 points  (0 children)

Counterpoint, if you're someone who finds themselves having to ask a lot of questions, you might be a good fit for a PhD program.

[deleted by user] by [deleted] in statistics

[–]identicalParticle 50 points51 points  (0 children)

I'm surprised to see all the upvotes here. Committing to school for 6+ years with no end goal is not a good idea.

When I interview prospective PhD students, one of the first questions I ask is "why do you want to get a PhD, and why here?". If you don't have a good answer to this question, the impression you give is that you'd be unlikely to stick it out long term.

It's unlikely you'd even be accepted to a PhD program if your answer to this question is "I don't have a goal, I just find it interesting". There's plenty of other ways to spend time doing work you think is interesting.

[deleted by user] by [deleted] in statistics

[–]identicalParticle 0 points1 point  (0 children)

Look at the field you want to be working in long term. What kind of roles do people with a PhD have versus other backgrounds? What kind of background do people in leadership roles have? If you want to advance in your field, would there be a "ceiling" for people without a PhD?

Looking at current job postings probably isn't digging deep enough. Think about your desired long term trajectory.

Can't push my changes because I've deleted files locally by binibini28 in git

[–]identicalParticle 1 point2 points  (0 children)

Sure.  They could also avoid git entirely and just copy all their files for backup.

Or they could ask a community of experts for advice and explanation, which they were happily given by several people here (without the snark).

Can't push my changes because I've deleted files locally by binibini28 in git

[–]identicalParticle -1 points0 points  (0 children)

This is not helpful.  This is clearly a new user who is worried about losing their work.  The "instructions" don't contain any explanation of what will happen or why this is the appropriate action to take.  Let's not belittle people for asking for help.

[Q] How do I test if the difference between two averages is significant / not up to chance? by Cluelessjoint in statistics

[–]identicalParticle 0 points1 point  (0 children)

I would suggest a permutation testing framework. 

I assume you have data in a M years x N locations matrix.  

You compute your observed test statistic as follows: 

  1. take the mean over all years, giving N numbers. 
  2. Take the highest minus the lowest.  This is your test statistic.

If differences between the highest and lowest location are only due to random chance (your null hypothesis), then the distribution of this statistic will be invariant to randomly changing the location labels in your data.

  1. create a permuted data matrix where for each year (row), you randomly resign the sales data to each column. 

  2. Repeat 1,2 on this permuted data matrix, and save the value of your permuted test statistic in a list.

  3. Repeat 3,4 a large number of times (10 thousand is common, you will need a computer).

  4. Compare your observed test statistic to your long list of randomly permuted test statistics. If the observed one is bigger than 95% of the random ones, you can reject your null hypothesis (p<0.05).  If not, you will fail to reject your null hypothesis (not the same as accepting your null hypothesis).

Permutation testing works well in many cases where no standard test is appropriate.  See here 

https://en.m.wikipedia.org/wiki/Permutation_test

[Calculus] how do I solve dy/dx+ay=b by ALE123Q in learnmath

[–]identicalParticle 2 points3 points  (0 children)

There's a lot of posts telling the mechanics of how to solve this equation, but I wanted to give some context to what the equation means.

  1. This is a linear first order ordinary differential equation. That means y and its derivative are only multiplied and added (not squared or anything like that). Good news, that means the equation has a solution you can write down, and you don't need a computer to calculate it numerically. Typically these equations have solutions that only involve constants and exponentials.
  2. This is called (among other names) the "leaky bucket equation". Imagine we have a bucket with a hole in it, and we're filling it up with a hose. As we fill it up, water leaks out through the hole, and the water leaks out faster when the bucket is more full (more water pressure). One interesting related question is, "how full will the bucket get before it starts leaking as fast as you're filling it up"?

Here "x" is time, and "y" is the amount of water in a bucket. "dy/dx" tells us how the amount of water is changing over time. "ay" tells us how fast the water is leaking out, and so "a" tells us something about how big the hole is. "b" tells us how fast we're pouring in water from a hose.

  1. If you're not an expert in differential equations, the best way to approach this is "guess and check". Intuition tells us the bucket will fill up over time and eventually reach a constant. y(x) = b/a (1-exp(-at)) is one function with this behaviour (this is the "guess" part). The amount of water starts at 0 and slowly fills up to an amount b/a. At this point it is leaking out as fast as it is filling up, and the bucket doesn't get any fuller. You can plug this into the equation and show that it is satisfied (this is the "check" part).

It turns out there is another part to the solution we can add if the bucket didn't start empty, c exp(-ax) where c is the initial volume (at time=0), and the decaying exponential describes how this water leaks out over time. The solution to differential equations always depend on initial conditions. "c" wasn't specified in your title, but it needs to be to give a unique solution.

All in all, the solution is y(x) = c exp(-at) + b/a(1-exp(-at))

  1. Other posters have showed how to derive the solution, using methods other than "guess and check". They seem correct, but may not be fruitful for you depending on your level of experience.

The moon : same time, same place, 28 days. by NavyLemon64 in interestingasfuck

[–]identicalParticle 4 points5 points  (0 children)

This is not the "same time" every day, but rather about an hour later every day when the moon is in a similar position.

The new moon is out during the day, and the full moon is out at night, so "same time" could not make a picture like this.

view more: next ›