论文标题
依赖的贝叶斯镜头：带有规范贝叶斯反演的双向马尔可夫内核的类别
Dependent Bayesian Lenses: Categories of Bidirectional Markov Kernels with Canonical Bayesian Inversion
论文作者
论文摘要
我们概括了现有的贝叶斯镜头的构造，以在对象对象依赖于前向对象上的状态（解释为概率分布）之间的对象之间接受镜头。这提供了一种自然的环境，用于研究贝叶斯倒置的随机图，仅限于给定先验支持的点。为了正式说明这一点，我们开发了Markov类别中支持对象的Fritz提出的定义，并表明这些定义会导致一部分，分为依赖的贝叶斯镜头类别，编码了贝叶斯倒置的更具规理的概念。
We generalise an existing construction of Bayesian Lenses to admit lenses between pairs of objects where the backwards object is dependent on states on the forwards object (interpreted as probability distributions). This gives a natural setting for studying stochastic maps with Bayesian inverses restricted to the points supported by a given prior. In order to state this formally we develop a proposed definition by Fritz of a support object in a Markov category and show that these give rise to a section into the category of dependent Bayesian lenses encoding a more canonical notion of Bayesian inversion.
