Sean Pinkney

After reading the copy-editors promptly re-calibrated their workflow

A hawk's quick shadow?
Body taken, head left here—
life owes no warning.

YouTube video by Milan Records USA Peter Peter - It's You | Heated Rivalry (Original Series Soundtrack)

"Is ok?" youtu.be/DoQyMsBHE-M?...

I do believe X is a threat

I checked for n>=3. For n = 3, the max is 4 which must be true.

Ah yep, just working under duress (kids)

I didn't prove max but I'm not sure it can be bigger. Let me know what's right!

2 cases bc of median. Max element is then

Even: n + n / 2
Odd: 2 n - (n + 1) / 2

Assuming positive integers for all n. The formula isn't too hard to work out afterward for the entire sequence

Wonderful apotropaic marks!

Halloween @mc-stan.org

Try this computing.stat.berkeley.edu/tutorial-par...

I'll take one if you have any left

Which base measure are we in?

So go re-run your models with simplex and report back with your results!

and to be comparable, if not a bit better, in all other cases.

What has changed is that we use the sum-to-zero vector transform and then softmax it. There are still n-1 free variables but the over/underflow for finite precision computing is less likely. 2/

Stan 2.37 will be out sometime next week. I helped get the updated simplex parameterization and sum-to-zero matrix (both rows and columns sum to zero).

We found the new simplex parameterization to be much better in the extremes of the simplex and across very small alpha values of the dirichlet 1/

Ask me any questions you may have about this! #stats

Q

This is the pre-pre-print of the meta-meta-analysis

I've come on here after months away to let everyone know that I apparently really like trance music

Oh I first read this as the water cycle is changing as a good thing rather than a permanent thing

Hi!

Are there any other statistics nerds in the NoVA area that want to get together?

😈

Updated cholesky corr parameterization testing Promising results from the Pymc folks Confirmation that the det Jacobian is correct and early tests show improved sampling!

Promising results from the Pymc folks! #bayesian #pymc #stats #stan

discourse.mc-stan.org/t/updated-ch...

Hi I don't know you, you don't know me but I have the same feels. Come say hi sometime to me

Please let me know! I'd love to see timing and ESS comparisons

It's similar to what Adam Haber came up with for tfp but it seems to be slightly better in small dimensions and much faster in large (100+) though this could be because of Stan's implementation vs tfp.