Bootstrap 4.3.1

[Denisha Simcoe]

Additionallydaily email digests are enabled for these users so they are pinged to return to the site a bit more urgently. Once bootstrap mode is turned off (which happens automatically once you have more than 50 members), this digest setting is reverted to its default.

I am attempting to create a bootstrap USB on a PA-200. The Palo site provides pretty good procedure documentation, but not much for troubleshooting. I have managed to create the bootstrap.tar.gz file and upload it to a PA-200 running Pan-OS 8.0.3. I have tried 4 different brands/models of USB sticks, but when running the create bootstrap process every one errors out with the following message:

Searches on the Palo support site and Google don't turn up anything specific to this error, or troubleshooting an externally connected device. I did find the list of officially supported USB devices for this process. I have been unable to find any of these locally so far, so I bought a couple others of the right make and model, but different capacities (larger).

My first question is whether the Palo appliances are really finicky to the point that only one of the listed USB sticks will work. If so, I will order them, but I'd like to get some feedback from others before buying more USB devices.

My second question is whether results might be different with a different model appliance. PA-200 is all I have available to me in the office at the moment, but we should have a PA-500 coming back in from a remote office in the near future (month or two) that I can try it on.

I am also experiencing the same issue. Have tried 2 usb drives, not from the supported list, and 1 supported which is a Kingston SE9 16gb usb 3.0. It is weird as I have already tried preparing the sticks on 2 PA850s.

Interestingly I actually realized about a month or two ago after attempting to update our bootstrap files that the same model of Kingston drives that we have always used no-longer functioned correctly. It isn't a big issue for us, so I haven't looked into it at all.

Thankfully I have a large amount of the Kingston DataTraveler G4s that I originally setup and kept updated. Since I started running into issues I simply stopped updating them as they still do the job fine, I simply can't update the bootstrap files.

Bootstrapping is the process of preparing your AWS environment for usage with the AWS Cloud Development Kit (AWS CDK).Before you deploy a CDK stack into an AWS environment, the environment must first be bootstrapped.

Resources and their configuration that are used by the CDK are defined in an AWS CloudFormation template. Thistemplate is created and managed by the CDK team. For the latest version of this template, see bootstrap-template.yaml in the aws-cdk GitHubrepository.

To bootstrap an environment, you use the AWS CDK Command Line Interface (AWS CDK CLI) cdk bootstrapcommand. The CDK CLI retrieves the template and deploys it to AWS CloudFormation as a stack, known as thebootstrap stack. By default, the stack name is CDKToolkit. By deploying thistemplate, CloudFormation provisions the resources in your environment. After deployment, the bootstrap stack will appear inthe AWS CloudFormation console of your environment.

I recently used bootstrapping to estimate confidence intervals for a project. Someone who doesn't know much about statistics recently asked me to explain why bootstrapping works, i.e., why is it that resampling the same sample over and over gives good results. I realized that although I'd spent a lot of time understanding how to use it, I don't really understand why bootstrapping works.

Specifically: if we are resampling from our sample, how is it that we are learning something about the population rather than only about the sample? There seems to be a leap there which is somewhat counter-intuitive.

I have found a few answers to this question here which I half-understand. Particularly this one. I am a "consumer" of statistics, not a statistician, and I work with people who know much less about statistics than I do. So, can someone explain, with a minimum of references to theorems, etc., the basic reasoning behind the bootstrap? That is, if you had to explain it to your neighbor, what would you say?

You want to ask a question of a population but you can't. So you take a sample and ask the question of it instead. Now, how confident you should be that the sample answer is close to the population answer obviously depends on the structure of population. One way you might learn about this is to take samples from the population again and again, ask them the question, and see how variable the sample answers tended to be. Since this isn't possible you can either make some assumptions about the shape of the population, or you can use the information in the sample you actually have to learn about it.

Imagine you decide to make assumptions, e.g. that it is Normal, or Bernoulli or some other convenient fiction. Following the previous strategy you could again learn about how much the answer to your question when asked of a sample might vary depending on which particular sample you happened to get by repeatedly generating samples of the same size as the one you have and asking them the same question. That would be straightforward to the extent that you chose computationally convenient assumptions. (Indeed particularly convenient assumptions plus non-trivial math may allow you to bypass the sampling part altogether, but we will deliberately ignore that here.)

This seems like a good idea provided you are happy to make the assumptions. Imagine you are not. An alternative is to take the sample you have and sample from it instead. You can do this because the sample you have is also a population, just a very small discrete one; it looks like the histogram of your data. Sampling 'with replacement' is just a convenient way to treat the sample like it's a population and to sample from it in a way that reflects its shape.

This is a reasonable thing to do because not only is the sample you have the best, indeed the only information you have about what the population actually looks like, but also because most samples will, if they're randomly chosen, look quite like the population they came from. Consequently it is likely that yours does too.

For intuition it is important to think about how you could learn about variability by aggregating sampled information that is generated in various ways and on various assumptions. Completely ignoring the possibility of closed form mathematical solutions is important to get clear about this.

+1 to @ConjugatePrior, I just want to bring out one point which is implicit in his answer. The question asks, "if we are resampling from our sample, how is it that we are learning something about the population rather than only about the sample?" Resampling is not done to provide an estimate of the population distribution--we take our sample itself as a model of the population. Rather, resampling is done to provide an estimate of the sampling distribution of the sample statistic in question.

This is probably a more technical explanation aimed at people who understand some statistics and mathematics (calculus, at least). Here's a slide from a course on survey bootstraps that I taught some while ago:

Now, instead of just going one way along the arrows, and losing some information/accuracy along these arrows, we can go back and say something about variability of $\hat\theta_n^*$ around $\hat\theta_n$.

Now, where does that 1000 samples, or whatever the magic number might be, comes from? It comes from our inability to draw all $n^n$ samples, so we just take a random subset of these. The right most "simulate" arrow states another approximation that we are making on our way to get the distribution of $\hat\theta_n$ around $\theta$, and that is to say that our Monte Carlo simulated distribution of $\hat\theta_n^(*r)$ is a good enough approximation of the complete bootstrap distribution of $\hat\theta_n^*$ around $\hat\theta_n$.

I am answering this question because I agree that this is a difficult thing to do and there are many misconceptions. Efron and Diaconis attempted to do that in their 1983 Scientific American article and in my view they failed. There are several books out now devoted to the bootstrap that do a good job. Efron and Tibshirani do a great job in their article in Statistical Science in 1986. I tried especially hard to make bootstrap accessible to practitioner's in my bootstrap methods book and my introdcution to bootstrap with applications to R. Hall's book is great but very advanced and theoretical. Tim Hesterberg has written a great supplemental chapter to one of David Moore's introductory statistics books. The late Clifford Lunneborg had a nice book. Chihara and Hesterberg recently came out with an intermediate level mathematical statistics book that covers the bootstrap and other resampling methods. Even advanced books like Lahiri's or Shao and Tu's give good conceptual explanations. Manly does well with his book that covers permutations and the bootstrap There is no reason to be puzzled about the bootstrap anymore. It is important to keep in mind that the bootstrap depends on the bootstrap principle "Sampling with replacement behaves on the original sample the way the original sample behaves on a population. There are examples where this principle fails. It is important to know that the bootstrap is not the answer to every statistical problem.

Through bootstrapping you are simply taking samples over and over again from the same group of data (your sample data) to estimate how accurate your estimates about the entire population (what really is out there in the real world) is.

If you were to take one sample and make estimates on the real population, you might not be able to estimate how accurate your estimates are - we only have one estimate and have not identified how this estimate varies with different samples that we might have encountered.

With bootstrapping, we use this main sample to generate multiple samples. For example, if we measured the profit every day over 1000 days we might take random samples from this set. We might the profit from one random day, record it, get the profit from another random day (which might happen to be the same day as before - sampling with replacement), record it, and so forth, until we get a "new" sample of 1000days (from the original sample).

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