A small tool to view real-world ActivityPub objects as JSON! Enter a URL
or username from Mastodon or a similar service below, and we'll send a
request with
the right
Accept
header
to the server to view the underlying object.
{
"@context": [
"https://www.w3.org/ns/activitystreams",
{
"ostatus": "http://ostatus.org#",
"atomUri": "ostatus:atomUri",
"inReplyToAtomUri": "ostatus:inReplyToAtomUri",
"conversation": "ostatus:conversation",
"sensitive": "as:sensitive",
"toot": "http://joinmastodon.org/ns#",
"votersCount": "toot:votersCount"
}
],
"id": "https://sigmoid.social/users/rope/statuses/109738646610851415/replies",
"type": "Collection",
"first": {
"id": "https://sigmoid.social/users/rope/statuses/109738646610851415/replies?page=true",
"type": "CollectionPage",
"next": "https://sigmoid.social/users/rope/statuses/109738646610851415/replies?only_other_accounts=true&page=true",
"partOf": "https://sigmoid.social/users/rope/statuses/109738646610851415/replies",
"items": [
{
"id": "https://sigmoid.social/users/rope/statuses/109738649308308193",
"type": "Note",
"summary": null,
"inReplyTo": "https://sigmoid.social/users/rope/statuses/109738646610851415",
"published": "2023-01-23T13:01:41Z",
"url": "https://sigmoid.social/@rope/109738649308308193",
"attributedTo": "https://sigmoid.social/users/rope",
"to": [
"https://www.w3.org/ns/activitystreams#Public"
],
"cc": [
"https://sigmoid.social/users/rope/followers"
],
"sensitive": false,
"atomUri": "https://sigmoid.social/users/rope/statuses/109738649308308193",
"inReplyToAtomUri": "https://sigmoid.social/users/rope/statuses/109738646610851415",
"conversation": "tag:sigmoid.social,2023-01-23:objectId=5344355:objectType=Conversation",
"content": "<p>In short, a Bayesian structure score is simply the marginal likelihood of the structure G, integrating over model parameters θ: p(data | G) = ∫ p(data | G, θ) p(θ | G) dθ. Integrating over all parameterizations θ effectively protects against overfitting. A cool result is that, under certain assumptions, this score can be computed in closed form in Bayesian networks, see e.g. the classical paper by Heckerman, Geiger and Chickering, (1995).</p>",
"contentMap": {
"en": "<p>In short, a Bayesian structure score is simply the marginal likelihood of the structure G, integrating over model parameters θ: p(data | G) = ∫ p(data | G, θ) p(θ | G) dθ. Integrating over all parameterizations θ effectively protects against overfitting. A cool result is that, under certain assumptions, this score can be computed in closed form in Bayesian networks, see e.g. the classical paper by Heckerman, Geiger and Chickering, (1995).</p>"
},
"attachment": [],
"tag": [],
"replies": {
"id": "https://sigmoid.social/users/rope/statuses/109738649308308193/replies",
"type": "Collection",
"first": {
"type": "CollectionPage",
"next": "https://sigmoid.social/users/rope/statuses/109738649308308193/replies?min_id=109738651110395806&page=true",
"partOf": "https://sigmoid.social/users/rope/statuses/109738649308308193/replies",
"items": [
"https://sigmoid.social/users/rope/statuses/109738651110395806"
]
}
},
"likes": {
"id": "https://sigmoid.social/users/rope/statuses/109738649308308193/likes",
"type": "Collection",
"totalItems": 0
},
"shares": {
"id": "https://sigmoid.social/users/rope/statuses/109738649308308193/shares",
"type": "Collection",
"totalItems": 0
}
}
]
}
}