Blaise: A Toolkit for High-Performance Probabilistic Inference. Blaise is a toolkit for high performance probabilistic inference, implemented in Java. Blaise provides efficient implementations of the algorithmic and representational primitives for the computations arising in probabilistic inference, along with means of composition that support easy incremental development of high-performance algorithms. Finally, Blaise is designed to allow easy interactive development with sophisticated visualization tools, so you can watch your computations unfold during development and debugging without sacrificing performance during production executions. Development on Blaise has recently focused on the implementation of stochastic search processes such as Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (particle filtering). Several other features are soon to be added, such as automatic parallelization for multicore processors and computing clusters, and inference schemes based on variational methods and message passing.