Unverified50% confidenceFactExact time
Under the Bayesian framework, when prior information is insufficient, the set of posterior distributions induced by all reasonable priors constitutes a credal set.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedMean-field variational inference assumes that the posterior distribution can be decomposed into a product of mutually independent Gaussian distributions for each parameter, sacrificing the ability to capture correlations between parameters.76% similarUnverifiedIn the Bayesian framework, the prior plays the role of regularization, and adding a regularization term is essentially equivalent to introducing a certain prior distribution for the parameters.75% similarUnverifiedWhen the likelihood function is non-Gaussian (e.g., count data, binary data, or heavy-tailed distributions), the posterior distribution of the latent field becomes analytically intractable even when the prior is a Gaussian process.74% similarUnverifiedThe researchers proposed a credal-set-based method in which the learner's prediction output is no longer a single probability distribution but a set of possible distributions presented in the form of a credal set.74% similarUnverifiedThe martingale posterior uses exchangeable predictive rules to directly generate posterior samples of clustering structures; as long as the predictive rules satisfy consistency conditions, probabilistic calibration of the posterior is guaranteed without requiring parametric assumptions about the data distribution.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1012844API
curl https://kongchang.com/api/v1/knowledge/claims/1012844MCP
get_claim(id=1012844)