当前位置: X-MOL 学术arXiv.cs.LO › 论文详情
Abstracting Probabilistic Models: A Logical Perspective
arXiv - CS - Logic in Computer Science Pub Date : 2018-10-04 , DOI: arxiv-1810.02434
Vaishak Belle

Abstraction is a powerful idea widely used in science, to model, reason and explain the behavior of systems in a more tractable search space, by omitting irrelevant details. While notions of abstraction have matured for deterministic systems, the case for abstracting probabilistic models is not yet fully understood. In this paper, we provide a semantical framework for analyzing such abstractions from first principles. We develop the framework in a general way, allowing for expressive languages, including logic-based ones that admit relational and hierarchical constructs with stochastic primitives. We motivate a definition of consistency between a high-level model and its low-level counterpart, but also treat the case when the high-level model is missing critical information present in the low-level model. We prove properties of abstractions, both at the level of the parameter as well as the structure of the models. We conclude with some observations about how abstractions can be derived automatically.
更新日期:2020-01-14

 

全部期刊列表>>
欢迎访问IOP中国网站
GIANT
产业、创新与基础设施
自然科研线上培训服务
材料学研究精选
胸腔和胸部成像专题
屿渡论文,编辑服务
何川
苏昭铭
陈刚
姜涛
李闯创
李刚
北大
跟Nature、Science文章学绘图
隐藏1h前已浏览文章
中洪博元
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
x-mol收录
上海纽约大学
张健
毛峥伟
陈芬儿
厦门大学
李祥
江浪
张昊
杨中悦
试剂库存
天合科研
down
wechat
bug