Pluck: Probabilistic Programming With Lazy Inference

Researchers released Pluck, a probabilistic programming language that uses lazy knowledge compilation for efficient exact inference. It supports higher-order functions, recursion, and can compute exact probability distributions and posterior samples for discrete random programs.

Pluck Pluck is a probabilistic programming language with efficient inference based on lazy knowledge compilation https://dl.acm.org/doi/10.1145/3729325 . It supports higher-order functions, many recursive programs, and recursive types. Quick Start Install Pluck and run your first program: In your shell: git clone --recurse-submodules https://github.com/mlb2251/Pluck.jl.git cd Pluck.jl Compile the rsdd library required for Pluck cd src/RSDD/rsdd cargo build --release --features ffi cd ../../.. In Julia: using Pkg Pkg.activate "." Pkg.instantiate using Pluck load pluck file "programs/simple example.pluck" ; Expressive, Lazy, Functional Programs Pluck programs are expressive functional programs, that can also make discrete random choices. It supports lazy algebraic data types, higher-order functions, and recursion. define-type tree Leaf Node nat tree tree define random-tree if flip 0.6 Leaf let left random-tree right random-tree Node geom 0.5 left right Node 2 Leaf Node 0 Leaf Leaf Leaf Node 1 Leaf Leaf Exact Inference Pluck can compute the exact probability distributions of many expressions. ;; What is the probability that a random tree has fewer than 4 nodes? query tree-size-small Marginal < length nodes random-tree 4 tree-size-small: true 0.855 false 0.145 Bayesian Conditioning Pluck can also compute the exact conditional i.e., posterior distribution of an expression, given some Boolean condition. query bst-given-size8-bounded let t random-tree Posterior isBST t and == size t 8 all-nodes-between t 0 10 bst-given-size8-bounded: false 0.9999999999998865 true 1.1346479311669095e-13 Posterior Sampling Pluck can draw exact samples from posterior distributions, even in infinite spaces. query tree-samples-given-size8-bst-bounded let t random-tree PosteriorSamples t and == size t 8 all-nodes-between t 0 10 isBST t 5 Node 9 Node 7 Node 6 Node 4 Node 3 Node 2 Node 1 Leaf Leaf Leaf Leaf Node 5 Leaf Leaf Leaf Leaf Leaf Node 8 Node 5 Node 4 Node 2 Node 1 Leaf Leaf Node 3 Leaf Leaf Leaf Node 6 Leaf Leaf Node 9 Leaf Leaf Node 8 Node 7 Node 5 Node 1 Leaf Node 4 Node 2 Leaf Node 3 Leaf Leaf Leaf Node 6 Leaf Leaf Leaf Leaf Node 8 Node 6 Node 3 Node 1 Leaf Node 2 Leaf Leaf Node 4 Leaf Node 5 Leaf Leaf Node 7 Leaf Leaf Leaf Node 8 Node 7 Node 6 Node 1 Leaf Node 3 Node 2 Leaf Leaf Node 5 Node 4 Leaf Leaf Leaf Leaf Leaf Leaf