Author Archives: drewancameron

How to push back against bullies in academia

I’m sure most of us know of at least one notorious workplace bully (or other type of shithead) in our field; the kind of professor about whom there’s an open secret of their bad behaviour, but given that universities don’t … Continue reading

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Copula models for astronomical distributions

Yesterday I read through this new arXival by an old friend from my ETH Zurich days, in which is presented a package (called LEO-py) for likelihood-based inference in the case of Gaussian copula models and linear regressions with missing data, … Continue reading

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Realizing the potential of astrostatistics and astroinformatics

I had a quick read of the new whitepaper from which this post borrows its title.  The main recommendation is for greater funding towards astrostatistics/informatics education which I would generally support.  Except that I feel one of our main barriers … Continue reading

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Random forests for exoplanets

One of today’s arXivals examines the use of random forest regression to predict exoplanetary radii given a training set with approx 10 observed properties of exoplanets and their host stars.  I don’t quite understand the motivation for a random forest … Continue reading

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Data compression for cosmology

Today I read an arXival on the topic of data compression or likelihood compression for cosmology, which translates to construction of approximately (or exactly) sufficient summary statistics via a transformation with reference to the assumed likelihood function.  As Alsing & … Continue reading

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A much improved approach to simulation based inference for cosmology, or maybe some sleight-of-hand?

A recent arXival presents an alternative method for approximating the posterior of some cosmological parameters given an observational dataset for which it is also required to marginalise over a set of nuisance parameters describing the non-linear regime. Previous approaches have … Continue reading

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Conditional density estimation tools for astronomy …

A recent arXival presents a (the arXival authors’ favourite) family of conditional density estimation (CDE) methods as a system for (A) propagating uncertainties downstream in astronomical analyses and (B) conducting likelihood-free inference in astronomy.  While some of the individual methods and … Continue reading

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