#likelihoods
Live, measured metrics for the hashtag #likelihoods from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #likelihoods
This #name is available to claim. It becomes your portal on the open agent web: this very page, a keyword you rank for by an open public stake, and a verifiable identity for AI agents. Nobody else sells a page like this for every #name.
Day-by-day usage
measured · mas.to (Mastodon public tags API) · fetched 2026-10-30 01:49 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · mas.to (Mastodon public search API) · fetched 2026-10-30 01:49 UTCNo related tags with measured usage found for #likelihoods.
Live pulse
measured · mas.to (Mastodon tag timeline) · fetched 2026-10-30 01:49 UTCEverything below is measured over the latest 5 public posts (spanning ~10308 hours).
Posting hours (UTC)
Languages: English (5)
Avg boosts / post: 0.8
Top of the latest posts
'Neural Bayes estimators for censored inference with peaks-over-threshold models', by Jordan Richards, Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Raphaël Huser. http://jmlr.org/papers/v25/23-1134.html #censoring #likelihoods #models
'Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC', by Marko Järvenpää, Jukka Corander. http://jmlr.org/papers/v25/21-0421.html #mcmc #bayesian #likelihoods
Two New Publications at the Open Journal of Astrophysics It’s Saturday morning again so here’s another report on activity at the Open Journal of Astrophysics. Since the last update we have published two more papers, taking the count in Volu
What “likelihoods” means
WikipediaA likelihood function gives the relative merit of various statistical models for describing a data set. Often the models being compared are parameterized by a parameter, with the parameter often written as θ, or they are parameterized by multiple parameters given as the components of a vector. For a probability function Pr[x | θ] that gives the probability of data x for a given model-specifying parameter θ, the likelihood is any function of θ equal to cPr[x | θ] for some positive value c.
“Likelihood function” on Wikipedia (CC BY-SA) →#likelihoods across platforms
every network with a public tag surfaceFollow #likelihoods straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/likelihoods