覆盖序列的语境模型
Context models on sequences of covers
摘要：
我们提出了一类模型，通过一个简单的结构，使精确的，增量的，非参数的，多项式时间，贝叶斯推断的条件措施。该方法依赖于在条件变量上创建一系列覆盖，并为覆盖内的每个集合维护不同的模型。通过在覆盖序列内指定随机游动的概率模型，推理仍然是容易处理的。我们证明了问题的条件密度估计，这是我们所知的第一个封闭形式，非参数贝叶斯方法来解决这个问题的方法。
英文摘要：
We present a class of models that, via a simple construction, enables exact, incremental, non-parametric, polynomial-time, Bayesian inference of conditional measures. The approach relies upon creating a sequence of covers on the conditioning variable and maintaining a different model for each set within a cover. Inference remains tractable by specifying the probabilistic model in terms of a random walk within the sequence of covers. We demonstrate the approach on problems of conditional density estimation, which, to our knowledge is the first closed-form, non-parametric Bayesian approach to this problem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
