The concept of trust regions used in VISA could be extended or adapted for other probabilistic programming systems beyond those discussed in this context by incorporating domain-specific knowledge or constraints into defining trust region boundaries. For instance:
In hierarchical Bayesian models where certain parameters are known or expected to exhibit specific relationships (e.g., correlations), customized trust region definitions could enforce these constraints during optimization.
For models involving structured data representations like graphs or time series data, trust regions could be tailored based on graph properties or temporal dependencies.
Trust regions might also be adapted based on prior information about certain parameters' ranges or distributions within specific application domains.
By integrating such domain-specific considerations into defining trust regions within probabilistic programming systems outside traditional settings like VI-based methods discussed here,VISA's adaptability could be enhanced for diverse modeling scenarios requiring nuanced control over optimization processes through well-defined boundaries influenced by contextual factors unique to each problem domain.