Abstract: A method, implemented on at least one computing device, for executing program code of a probabilistic programming language. The program code comprises a series of statements including random procedures for which values are determined when the random procedures are executed, and constraints on results obtained when executing the program code. An execution history for the program code comprises a stored set of values provided for random procedures during execution of the program code. The method comprises generating a plurality of execution histories for the program code. A subset of execution histories from a set comprising the plurality of generated execution histories is determined, using at least one constraint of the program code. At least one new execution history is generated by copying the at least one execution history, and the steps are then repeated using the determined subset of execution histories and the at least one new execution history.

Abstract: Circuits that solve stochastic problems and techniques for operating them. These natively stochastic circuits may produce samples from probability distributions of interest for particular stochastic problems, and may be combined together in any suitable way to yield potential solutions to stochastic problems. In some implementations, the stochastic circuits may generate samples from conditional probability distributions conditioned on input data provided to the stochastic circuits. The circuits may be constructed from multiple interconnected stochastic subcircuits such that a circuit may produce a sample from a joint probability distribution, or from a marginal distribution of a joint distribution. These circuits may be used to implement stochastic sampling algorithms to solve stochastic processes, and may include stochastic subcircuits that operate concurrently.

Abstract: Circuits that solve stochastic problems and techniques for operating them. These natively stochastic circuits may produce samples from probability distributions of interest for particular stochastic problems, and may be combined together in any suitable way to yield potential solutions to stochastic problems. In some implementations, the stochastic circuits may generate samples from conditional probability distributions conditioned on input data provided to the stochastic circuits. The circuits may be constructed from multiple interconnected stochastic subcircuits such that a circuit may produce a sample from a joint probability distribution, or from a marginal distribution of a joint distribution. These circuits may be used to implement stochastic sampling algorithms to solve stochastic processes, and may include stochastic subcircuits that operate concurrently.