Roadmap 0.1.0

[rlouf]

• Documentation
• Sample (selected) deterministic variables
• HMC from blackjax-devs/blackjax
• NUTS from blackjax-devs/blackjax
• Effective sample size from blackjax-devs/blackjxa
• Models are distirbutions
• Fix bug with rng keys in forward sampling Incorrect results when sampling from the prior #90
• Refactor the core (Simplify the access to the graph's nodes #4)
• Save the kernel parameters after warmup #98
• document the core extensively
• Rhat (online estimation)
• Average acceptance rate
• Number of divergences
• Inference summary

[rlouf]

What it is not about:

• Warmup using Adam instead of Dual Averaging
• Dynamical HMC
• Discontinuous HMC
• Riemannian manifold Hamiltonian Monte Carlo
• Metropolis-within-Gibbs
• Particle Filtering / Sequential Monte Carlo
• HMC augmented with NN
• HMC with Neural Transport
• More distributions
• Stochastic processes
• Simulation-based calibration
• Population predictive check
• Rao-Blackwellization
• Automatic reparametrization
• Bayesian Neural Networks (by subclassing trax) -> 4d5305a
• Measure effective sample size with regenerative methods
• Stochastic gradient HMC which could be the first use of a sequential sampler. Also can be used for stochastic conditioning.

[elanmart]

Hi @rlouf , I was searching for the do-operator recently, and it seems it got removed at some point.

Are there any plans to add it back? It is marked as "done" in the roadmap above. I can hack around it, but it would be super useful for some code examples in Rethinking 😅

[rlouf]

I took it off because the implementation would be a lot different; the design I had in my was that when we use .do(a=10) the variable a now becomes an argument to every target function and we partially apply these with a's value. Not as simple as before but I would definitely appreciate a PR adding this to MCX.