Hello,

Thank you in advance for reading. I am a researcher considering whether to use Turing for a project. As background, I will be implementing and extending a sparse infinite Bayesian factor model as described in Sparse Bayesian infinite factor models - PMC.

While this paper specifies the full conditional posterior distribution for each parameter and thus allows Gibbs sampling, I plan to introduce some additional latent variables that will require Metropolis-Hastings sampling within Gibbs. Thus, Turingâ€™s compositional sampler is very appealing.

**My question is, can I use the MH() functionality in Turing to specify the conditional posterior as a proposal and force an acceptance rate of 1 for the parameters whose conditional posteriors I know**? I will use either MH() or NUTS() for the remaining parameters. As far as I can tell it is possible to specify the proposal for MH() but I did not see an obvious way to force Turing to accept the proposal to emulate traditional Gibbs sampling.

Thank you for your time.

Best,

Aaron