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I've been using pretty standard Gibbs/Metropolis-Hastings samplers that I hand-coded in order to do Bayesian inference via Markov Chain Monte Carlo in order to fit some complex models. However, I'm aware that there are many significant algorithmic improvements available (e.g. this question: What are some well known improvements over textbook MCMC algorithms that people use for bayesian inference?What are some well known improvements over textbook MCMC algorithms that people use for bayesian inference?).

Is there a well-known/commonly used software package that implements many of these techniques, that can nonetheless be used for an arbitrary black-box model (i.e. my existing simulation code, not a generalized linear model or anything likely to be pre-implemented)? This doesn't seem to be a feature of BUGS or Stan. I've found Mamba and PyMC - but they seem to require a bit more re-implementation of models than I'd like. Has anyone found software that allows straightforward implementation of inference on a black box function?

I've been using pretty standard Gibbs/Metropolis-Hastings samplers that I hand-coded in order to do Bayesian inference via Markov Chain Monte Carlo in order to fit some complex models. However, I'm aware that there are many significant algorithmic improvements available (e.g. this question: What are some well known improvements over textbook MCMC algorithms that people use for bayesian inference?).

Is there a well-known/commonly used software package that implements many of these techniques, that can nonetheless be used for an arbitrary black-box model (i.e. my existing simulation code, not a generalized linear model or anything likely to be pre-implemented)? This doesn't seem to be a feature of BUGS or Stan. I've found Mamba and PyMC - but they seem to require a bit more re-implementation of models than I'd like. Has anyone found software that allows straightforward implementation of inference on a black box function?

I've been using pretty standard Gibbs/Metropolis-Hastings samplers that I hand-coded in order to do Bayesian inference via Markov Chain Monte Carlo in order to fit some complex models. However, I'm aware that there are many significant algorithmic improvements available (e.g. this question: What are some well known improvements over textbook MCMC algorithms that people use for bayesian inference?).

Is there a well-known/commonly used software package that implements many of these techniques, that can nonetheless be used for an arbitrary black-box model (i.e. my existing simulation code, not a generalized linear model or anything likely to be pre-implemented)? This doesn't seem to be a feature of BUGS or Stan. I've found Mamba and PyMC - but they seem to require a bit more re-implementation of models than I'd like. Has anyone found software that allows straightforward implementation of inference on a black box function?

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Bayesian inference using MCMC - software for black box model

I've been using pretty standard Gibbs/Metropolis-Hastings samplers that I hand-coded in order to do Bayesian inference via Markov Chain Monte Carlo in order to fit some complex models. However, I'm aware that there are many significant algorithmic improvements available (e.g. this question: What are some well known improvements over textbook MCMC algorithms that people use for bayesian inference?).

Is there a well-known/commonly used software package that implements many of these techniques, that can nonetheless be used for an arbitrary black-box model (i.e. my existing simulation code, not a generalized linear model or anything likely to be pre-implemented)? This doesn't seem to be a feature of BUGS or Stan. I've found Mamba and PyMC - but they seem to require a bit more re-implementation of models than I'd like. Has anyone found software that allows straightforward implementation of inference on a black box function?