Un garçon pas comme les autres (Bayes)
Tail stabilization of importance sampling etimators: A bit of theory
Look. I had to do it so I wrote it out in detail. This is some of the convergence theory for truncated and winzorised importance sampling estimators
Sparse matrices 6: To catch a derivative, first you've got to think like a derivative
Open up the kennels, Kenneth. Mamma’s coming home tonight.
Sparse Matrices 5: I bind you Nancy
A new JAX primitive? In this economy?
Design is my passion: sparse matrices part four
Just some harmeless notes. Like the ones Judy Dench took in that movie.
Sparse Matrices 3: Failing at JAX
_Takes a long drag on cigarette._ JAX? Where was he when I had my cancer?
Sparse Matrices 2: An invitation to a sparse Cholesky factorisation
Come for the details, stay for the shitty Python, leave with disappointment. Not unlike the experience of dating me.
Sparse Matrices 1: The linear algebra of linear mixed effects models and their generalisations
Hubris. Just hubris. But before the fall comes the statement of purpose. This is that statement.
Barry Gibb came fourth in a Barry Gibb look alike contest (Repost)
A repost from Andrew's blog about comparing computational methods for performing a task. (Lightly edited.) Original posted 20 October, 2017.
Why won't you cheat with me? (Repost)
A repost from Andrew's blog about how design information infects multivariate priors. (Lightly edited. Well, a bit more than lightly because the last version didn't fully make sense. But whatever. Blogs, eh.) Original posted 5 November, 2017.
The king must die (repost)
A repost (with edits, revisions, and footnotes) from Andrew's blog about how much I hate the Bayesian Lasso. Originally published 2nd November, 2017.
Getting into the subspace; or what happens when you approximate a Gaussian process
Fuck man, I don't know about this one. A lot of stuff happens. At some point there's a lot of PDEs. There are proofs. Back away.
Yes but what is a Gaussian process? or, Once, twice, three times a definition; or A descent into madness
Gaussian processes. As narrated by an increasingly deranged man during a day of torrential rain.
Priors: Fire With Fire (Track 3)
Objective priors? In finite dimensions? A confidence trick? Yes.
Priors: Whole New Way (Track 2)
Conjugate Priors? The crystal deoderant of Bayesian statistics
Priors: Night work (Track 1)
Priors? Defined. Questions? Outlined. Purpose? Declared.
$(n-1)$-sane in the membrane
Windmills? Tilted. Topic? Boring. (n-1)? No.