I think it would be interesting if frequentist stats can come up with more generative models. Current high level generative machine learning all rely on Bayesian modeling.

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jmalicki 11 hours ago | parent | next [-]

I'm not well versed enough, but what would a frequentist generative model even mean?

The entire generative concept implicitly assumes that parameters have probability distributions themselves that naturally give rise to generative models...

You could do frequentist inference on a generative model, sure, but generative modelling seems fundamentally alien to frequentist thinking?

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hawtads 11 hours ago | parent [-]

I am more familiar with Bayesian than frequentist stats, but given that they are mathematically equivalent, shouldn't frequentist stats have an answer to e.g. the loss function of a VAE? Or are generative machine learning inherently impossible to model for frequentist stats?

Though if you think about it, a diffusion model is somewhat (partially) frequentist.

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jmalicki 10 hours ago | parent | next [-]

I guess you have me thinking more... things like Parzen window estimators or other KDEs are frequentist...

But while it's a probability distribution, to a frequentist they are estimating the fixed parameters of a distribution.

The distribution isn't generative, it just represents uncertainty - and I think that's a bit of the deep core philosophical divide between frequentists and Bayesians - you might use all the same math, but you cannot possibly think of it as being generative.

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jmalicki 11 hours ago | parent | prev [-]

They do!

https://arxiv.org/pdf/2510.18777

But that doesn't mean a frequentist views a VAE as a generative model!

Putting it another way, Gaussian processes originated as a frequentist technique! But to a frequentist they are not generative.

	▲	hawtads 11 hours ago \| parent [-]
		Ooh good find, thanks for the link. This will be my bedtime reading for this week :)

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DeathArrow 7 hours ago | parent | prev [-]

Most ML algorithms, be it SVM, random forest or neural networks require parameter tuning. That in itself is using bayesian statistics.