Explore a 26-minute conference talk from Haskell 2023 that delves into using effect handlers for programmable inference in probabilistic programming. Learn how algebraic effects can provide a structured and modular foundation for inference algorithms, offering an alternative to monad transformers. Discover two abstract algorithms representing Metropolis-Hastings and particle filtering, and see how this approach reveals high-level structure and facilitates easy customization. Gain insights into implementing these inference patterns as a Haskell library and understand the advantages and disadvantages of algebraic effects compared to monad transformers in modular imperative algorithm design.