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# Monthly Archives: August 2019

## Polarizing Bayesian approaches: why literature matters [Guest post: J. Michael Burgess]

As Bayesian buzzwords continue to pop onto arXiv daily, one often runs across ad hoc methods for Monte Carlo integration of posteriors. Of course, there are ready-made tools that can be downloaded and easily used for most problems, still, one … Continue reading

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## Comparing the incomparable = polishing the unpolishable* [Guest post: J Michael Burgess]

Trying to produce works that both illuminate the proper use of statistical methodology and perhaps tackle some relevant scientific question is a tough game. Any of us that get comments from the referee such as “why are you using this … Continue reading

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## Neural flows for CMDs

An interesting arXival from last week describes a “Bayesian neural flow” (i.e., masked auto-encoder) model as a data-adaptive prior for estimation of the de-noised (latent) distribution of Gaia catalogue stars in colour-magnitude space. The advantage of neural flow models in … Continue reading

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## Approximate Bayesian Computation with heads in the sand

I noticed a recent arXival describing an ABC approach to inferring halo modal parameters from lensing substructure, which it seems is the third in a series of installments, each of which churlishly manages to pretend not to be aware of … Continue reading

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## Random Fourier features for X-ray variability

A recent arXival brought to my attention some interesting work (including an author who’s a friend of mine from our St Andrews days, so some personal bias here) modelling the time-series and frequency-dependent time-lags of X-ray sources. The model is … Continue reading

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## Cosmological GANs

A new arXival describes work to improve the computational efficiency of GANs (generative adversarial networks) for emulating cosmological N-body simulators. It seems to me that we’re putting the cart before the horse here. The notion is that since cosmological N-body … Continue reading

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## Errors-in-variables, or not …

One can see from this new arXival that errors-in-variables models have not yet become widely known within astronomy yet, though astronomers are trying to find ways to deal with this sort of modelling scenario. The errors-in-variables regression problem occurs when … Continue reading

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