The Logical Essentials of Bayesian Reasoning
This work offers an accessible introduction to a structured framework for Bayesian reasoning, potentially benefiting researchers and practitioners in fields like AI and statistics, but it appears incremental as it builds on existing methodologies.
The chapter introduces a channel-based approach to Bayesian probability theory, using algebraic and logical foundations inspired by programming language semantics to provide a uniform language for describing Bayesian phenomena, including inference in Bayesian networks modeled with string diagrams.
This chapter offers an accessible introduction to the channel-based approach to Bayesian probability theory. This framework rests on algebraic and logical foundations, inspired by the methodologies of programming language semantics. It offers a uniform, structured and expressive language for describing Bayesian phenomena in terms of familiar programming concepts, like channel, predicate transformation and state transformation. The introduction also covers inference in Bayesian networks, which will be modelled by a suitable calculus of string diagrams.