#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 · fosstodon.org (Mastodon public tags API) · fetched 2026-09-11 02:27 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · fosstodon.org (Mastodon public search API) · fetched 2026-09-11 02:27 UTCNo related tags with measured usage found for #likelihoods.
Live pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-09-11 02:27 UTCEverything below is measured over the latest 4 public posts (spanning ~10308 hours).
Posting hours (UTC)
Languages: English (4)
Avg boosts / post: 0.5
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
'MAP- and MLE-Based Teaching', by Hans Ulrich Simon, Jan Arne Telle. http://jmlr.org/papers/v25/23-1086.html #likelihoods #priors #likelihood
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