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Bayesian definition of random sequences with respect to conditional probabilities
arXiv - CS - Information Theory Pub Date : 2017-01-23 , DOI: arxiv-1701.06342 Hayato Takahashi
arXiv - CS - Information Theory Pub Date : 2017-01-23 , DOI: arxiv-1701.06342 Hayato Takahashi
We show that the posterior distribution converges weakly to ML-random
parameters when the model is consistent and define conditional random set by
the inverse of the Bayes estimator to validate consistent theorem in Bayes
models for individual ML-random parameters and conditional random sequences.
中文翻译:
关于条件概率的随机序列的贝叶斯定义
我们表明,当模型一致时,后验分布弱收敛于 ML 随机参数,并通过贝叶斯估计器的逆定义条件随机集,以验证贝叶斯模型中单个 ML 随机参数和条件随机序列的一致定理。
更新日期:2020-04-03
中文翻译:
关于条件概率的随机序列的贝叶斯定义
我们表明,当模型一致时,后验分布弱收敛于 ML 随机参数,并通过贝叶斯估计器的逆定义条件随机集,以验证贝叶斯模型中单个 ML 随机参数和条件随机序列的一致定理。