### Markovian Gaussian processes: A lot of theory and some practical stuff

Well this is gonna be technical. And yes, I’m going to define it three ways. Because that’s how comedy works.

### On that example of Robins and Ritov; or A sleeping dog in harbor is safe, but that’s not what sleeping dogs are for

Look, it’s a dull example of Bayes being bad. But it comes up often enough to be worth talking about. I’m going to, unsurprisingly, argue that Bayes isn’t bad. Neither are Robings/Ritov/Wasserman wrong. They’re just looking at the problem through a different lens.

### Priors for the parameters in a Gaussian process

If you’re not a machine learner (and sometimes if you are), Gaussian processes need priors on their parameters. Like everything else to do with Gaussian processes, this can be delicate. This post works through some options.

### A first look at multilevel regression; or Everybody’s got something to hide except me and my macaques

A small introduction to multilevel models. Why? Because I said so, that’s why. And you will simply not believe what happens to residual plots.

### 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 5: I bind you Nancy

A new JAX primitive? In this economy?

### Sparse Matrices 4: Design is my passion

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.

### 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)

### 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.