We present GenJAX, a new language and compiler for vectorized programmable probabilistic inference. GenJAX integrates the vectorizing map (vmap) operation from array programming frameworks such as JAX into the programmable inference paradigm, enabling compositional vectorization of features such as probabilistic program traces, stochastic branching (for expressing mixture models), and programmable inference interfaces for writing custom probabilistic inference algorithms. We formalize vectorization as a source-to-source program transformation on a core calculus for probabilistic programming ( λ GEN ), and prove that it correctly vectorizes both modeling and inference operations. We have implemented our approach in the GenJAX language and compiler, and have empirically evaluated this implementation on several benchmarks and case studies. Our results show that our implementation supports a wide and expressive set of programmable inference patterns and delivers performance comparable to hand-optimized JAX code.

This paper introduces SMCP3, a method for automatically implementing custom sequential Monte Carlo samplers for inference in probabilistic programs. Unlike particle filters and resample-move SMC (Gilks and Berzuini, 2001), SMCP3 algorithms can improve the quality of samples and weights using pairs of Markov proposal kernels that are also specified by probabilistic programs. Unlike Del Moral et al. (2006b), these proposals can themselves be complex probabilistic computations that generate auxiliary variables, apply deterministic transformations, and lack tractable marginal densities. This paper also contributes an efficient implementation in Gen that eliminates the need to manually derive incremental importance weights. SMCP3 thus simultaneously expands the design space that can be explored by SMC practitioners and reduces the implementation effort. SMCP3 is illustrated using applications to 3D object tracking, state-space modeling, and data clustering, showing that SMCP3 methods can simultaneously improve the quality and reduce the cost of marginal likelihood estimation and posterior inference.