#propositionalmodelcounting
Live, measured metrics for the hashtag #propositionalmodelcounting from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #propositionalmodelcounting
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Day-by-day usage
measured · mas.to (Mastodon public tags API) · fetched 2026-11-08 02:06 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 2026-11-08 02:06 UTCNo related tags with measured usage found for #propositionalmodelcounting.
Live pulse
measured · mas.to (Mastodon tag timeline) · fetched 2026-11-08 02:06 UTCEverything below is measured over the latest 2 public posts (spanning ~18152 hours).
Posting hours (UTC)
Languages: English (2)
Avg boosts / post: 1.5
Top of the latest posts
I'm teaching my first lecture at the new job today, about probabilistic logic programming, probabilistic inference, and (weighted) model counting. Some of the required reading is a paper (https://eccc.weizmann.ac.il/eccc-reports/2003/TR03-0
The results of the 2022 Model Counting competition have been published! https://github.com/msoos/model-counting-comp-2022 Thanks to Markus Hecher and Johannes Fichte for organising, and thanks to @msoos for uploading. Now go play with those
#propositionalmodelcounting across platforms
every network with a public tag surfaceFollow #propositionalmodelcounting 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/propositionalmodelcounting