infergo v0.3.0 is out.
- Only methods returning float64 or nothing are differentiated. This allows to define helper methods on the model, such as returning the number of parameters, and call the methods outside of differentiated context.
infergomodels can be optimized using Gonum optimization algorithms. This includes BFGS and variants. Case study lr-gonum applies L-BFGS to linear regression.
- Case studies have been extended. New
- linear regression, solved using either stochastic gradient descent and BFGS;
- compilation of
infergomodels and inference into WebAssembly and running in the browser;
- integration with Gonum;
- Neal’s funnel, a re-parameterization example borrowed from Stan documentation.