memo-lang 1.2.8

A probabilistic programming language for reasoning about reasoning

memo is a probabilistic programming language for expressing computational cognitive models involving recursive reasoning about reasoning. memo inherits from the tradition of WebPPL-based Bayesian modeling (see probmods, agentmodels, and problang), but aims to make models easier to write and run by taking advantage of modern programming language techniques and hardware capabilities (including GPUs!). As a result, models are often significantly simpler to express (we've seen codebases shrink by a factor of 3x or more), and dramatically faster to execute and fit to data (we've seen speedups of 3,000x or more). In idiomatic memo, a POMDP solver is 15 lines of code, and is just as fast as a hand-optimized solver written in 200 lines of code.

memo stands for: mental modeling, memoized matrix operations, model-expressed-model-optimized, and metacognitive memos.

For more information, please visit memo's GitHub repository.