In paper 'Denotational Validation of Higher-Order Bayesian Inference', the authors mentioned the two main challenges are: 1) Handling continuous distributions requires a semantic framework based on measure theory and Lebesgue integration, which introduces mathematical complexity. 2) Using higher-order functions in the semantic meta-language enables modular interpretation of probabilistic programs but creates tension with measure theory, as there is no compatible measurable space structure for arbitrary measurable functions that supports standard operations like function application. To solve such tension, they use a space proposed by another paper that supports both function spaces and continuous distributions. You've read the paper that introduced such space. Provide the full name of that paper.