We present λPSI, the first probabilistic programming language and system that supports higher-order exact inference for probabilistic programs with first-class functions, nested inference and discrete, continuous and mixed random variables. λPSI’s solver is based on symbolic reasoning and computes the exact distribution represented by a program.

We show that λPSI is practically effective—it automatically computes exact distributions for a number of interesting applications, from rational agents to information theory, many of which could so far only be handled approximately.

@inproceedings{gehr2020lpsi, title = {$\lambda${P}{S}{I}: {Exact} {Inference} for {Higher}-{Order} {Probabilistic} {Programs}}, isbn = {978-1-4503-7613-6}, url = {https://dl.acm.org/doi/10.1145/3385412.3386006}, doi = {10.1145/3385412.3386006}, booktitle = {Proceedings of the 41st {ACM} {SIGPLAN} {Conference} on {Programming} {Language} {Design} and {Implementation}}, publisher = {ACM}, author = {Gehr, Timon and Steffen, Samuel and Vechev, Martin}, month = jun, year = {2020} }