Unverified60% confidenceFactExact time
The posterior distribution is obtained via Bayes' theorem: p(θ|x) ∝ p(x|θ)·p(θ), combining prior knowledge with the data likelihood.
2
Sources
60%
Confidence
Long-term
Relevance
8/4/2026
First Seen
Sources
机器学习的数学基础：从Mitchell定义到概率视角全解析
Related Claims
UnverifiedBayesian evidence is defined as the marginal probability density of the observed data under a given model, i.e., the integral over the parameter prior: Z = ∫ p(x|θ,M) p(θ|M) dθ.75% similarUnverified重要性采样的数学核心是测度变换，通过引入提议分布q并用似然比w=p(x)/q(x)对样本重加权，无偏地估算原始概率72% similarUnverifiedThe Glivenko–Cantelli theorem guarantees that as the sample size increases, the empirical distribution function converges uniformly to the true distribution function.72% similarUnverifiedUnder the Bayesian framework, when prior information is insufficient, the set of posterior distributions induced by all reasonable priors constitutes a credal set.72% similarUnverifiedThe paper uses a tensor criterion to classify distribution families into three regimes: everywhere optimal (R=1), dimension-independent bounded overhead, and Θ(√log d) divergence with dimensionality.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/680943API
curl https://kongchang.com/api/v1/knowledge/claims/680943MCP
get_claim(id=680943)