What is the difference between rand and sample?

General Usage
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What is the difference between rand(array, n) and sample(array, n)?

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sample is from where?

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sample is from where?

StatsBase.sample(array, n) and StatsBase.rand(array, n)

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rand is from Base.

I don’t know how consistent people are about this, but as I think of it, rand is usually lower-level, while sample is often built in terms of (potentially many) calls to rand. sample is also often used for MCMC.

@Tamas_Papp I’ve heard you make similar arguments, anything to add?

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5

If you use @edit to inspect the code of calls to both functions you will probably reach the same understanding given by @cscherrer. It seems like sample calls a sample! that may call many different options, ranging from direct_sample (which then calls rand) or things like: samplepair, fisher_yates_sample!, self_avoid_sample!, and seqsample_c! (more complex ways of sampling, all probably built over rand).

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StatsBase.sample is for picking random items from a collection, with potentially weights and with/without replacement. I don’t think it was intended to be a generic function, it is a utility for a special case.

Random.rand is a generic IID sampler.

@jzr, all of this is documented, which part of the documentation did you find unclear?

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7

I don’t think it was intended to be a generic function, it is a utility for a special case.

My confusion arose because sample is indeed used as a generic function by Turing.jl and other packages. If I understand correctly, there are multiple interpretations of the distinction between these functions. (Namely, “rand is generic iid; sample isn’t generic”, and “rand is simple low-level drawing; sample is higher-level drawing that uses rand”.)

Would it have been appropriate usage for the MCMC packages to use rand for their user-facing sampling interface (instead of sample)?

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Personally I think that using StatsBase.sample for MCMC is bad style (“punning”).

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I fully agree with this view. The function sample refers to an operation performed on a dataset (or population) and can take weights and replacement options. The function rand is for random variables with a given distribution (e.g. Distributions.jl).

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@Tamas_Papp @juliohm what name would you use for a function that samples from a posterior distribution, given a model, observed data, and sampling algorithm?

Soss currently uses a different function name for each algorithm, but I like the idea of having the abstracted away a bit, so making the algorithm an argument is appealing to me.

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I think that Turing.sample is OK. It just should not coincide with StatsBase.sample, which is a rather pointless pun.

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@cscherrer I think I would end up choosing rand for this posterior distribution so that it is clear that it is not a sample from a population a la StatsBase.sample. I am trying to follow this pattern in my packages whenever I can so that users know when they can pass weights for example.

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And I would maybe describe this functionality as:

A function that draws from a posterior distribution given a model, observed data and algorithm…

But that is all a matter of taste I guess as we all understand the meaning of these concepts. It would be nice to have a consistent terminology across multiple ecosystems though. :slight_smile:

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I’m pretty sure Turing overrides and re-exports StatsBase.sample.

But these samples are not usually iid, which breaks a common assumption of rand. To me this is a more critical distinction than requiring that sample is only for drawing a sample from a population, which isn’t at all clear to me. Are you requiring here that a “population” be finite, and unweighted?

If someone uses rand thinking the results will be iid, violating that could cause big problems. As for sample, I really don’t see such a risk in extending it.

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Oh I had IID samples in mind when you wrote:

I am assuming they are not IID because of an algorithmic (MCMC) detail?

Yes, and I think that is what most people have in mind when they hear the term population in statistics, but I may be wrong.

I think the IID property is relevant, but isn’t the critical aspect to differentiate between rand and sample semantics.

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Yes, that’s right. Sometimes we can get IID samples, but that’s not usually the case.

Maybe it depends on which branch of statistics. The expression “sample from the posterior” comes up a lot in Bayesian stats, and we usually don’t assume the result will be IID.

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That said, StatsBase.sample is a utility function for a very specific stats/probability exercise, ie pulling objects from a bag/urn with/without replacement. This meaning predates the Bayesian/MCMC usage by centuries, and is really not the same thing.

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