others that read from its internal state or serve as inputs to its next transition are updating — is analogous to the so-called “dynamic discipline” that defines valid clock schedules for traditional sequential logic[@ward1990computation]. Deterministic and stochastic schedules, implementing cycle or mixture hybrid kernels[@andrieuintroduction], are both possible. This simple rule also implies a tremendous amount of exploitable parallelism in stochastic transition circuits: if two variables are independently caused given the current setting of all others, they can be updated at the same time.

Assemblies of stochastic transition circuits implement Bayesian reasoning in a straightforward way: by fixing, or “clamping” some of the variables in the assembly. If no variables are fixed, the circuit explores the full joint distribution, as shown in Figure 2E and 2F. If a variable is fixed, the circuit explores the conditional distribution on the remaining variables, as shown in Figure 2G and 2H. Simply by changing which transition circuits are updated, the circuit can be used to answer different probabilistic queries; these can be varied online based on the needs of the application.

[**(Figure 2 about here.)**]{} \[fig:transition\]

The accuracy of ultra-low-precision stochastic transition circuits. {#the-accuracy-of-ultra-low-precision-stochastic-transition-circuits. .unnumbered}
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The central operation in many Markov chain techniques 