Hi, My program BAli-Phy implements probabilistic programming with models written as Haskell programs. http://www.bali-phy.org/models.php Unlike [1], models are *lazy* in the sense that you can generate an infinite list of random variables, as long as you only access a finite number. I am also using MCMC for inference, not particle filters. BAli-Phy implements a Haskell interpreter in C++ that records execution traces of Haskell programs. When a random variable is modified (during MCMC), the parts of the execution trace that depend on the changed variables are invalidated and recomputed. This saves work by not recomputing the probability of the new state from scratch. Here's a example of a model written in Haskell: https://github.com/bredelings/BAli-Phy/blob/master/tests/prob_prog/regressio... WARNING: There are lots sharp edges. First and foremost, no type system! (yet) -BenRI [1] Practical probabilistic programming with monads, https://dl.acm.org/doi/10.1145/2804302.2804317